Image processing apparatus, image processing method, and program

ABSTRACT

An image processing apparatus includes: a representative value calculation unit that selects a designation area from a first image, and that calculates a representative value of each of the color components in the designation area, a class classification unit that performs class classification on the designation area, a coefficient reading unit that reads a coefficient that is stored in advance, a color component conversion unit that sets the pixel value relating to a predetermined pixel within the designation area to be a prediction tap, sets the pixel value of one color component, to be a reference, and converts the pixel value of each color component into a conversion value, and a product and sum calculation unit that sets the conversion value to be a variable and calculates each of the pixel values of a second image, by performing product and sum calculation which uses the coefficient which is read.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Japanese Priority PatentApplication JP 2013-074577 filed Mar. 29, 2013, the entire contents ofwhich are incorporated herein by reference.

BACKGROUND

The present technology relates to an image processing apparatus, animage processing method, and a program, and particularly to an imageprocessing apparatus, an image processing method, and a program that arecapable of obtaining an image signal of each color component from anoutput from an image sensor with a color filter array that is configuredfrom multiple color components, without degrading image quality whenperforming image processing for resolution conversion.

In recent years, there has been an increasing demand for an improvementin image resolution, such as so-called 4 k2 k or 8 k4 k. However, forexample, even though the size of an image sensor may be increased, it isdifficult to realize sufficient improvement in resolution in terms ofthe size, the weight, or the like of an optical system. Furthermore, itis also difficult to increase a frame rate in terms of sensitivity ofthe image sensor.

Because of this, a technology has been pursued in which resolution isimproved without decreasing an area of a cell of the image sensor tomore than a predetermined extent. For example, a technology has beenpursued in which a pixel of an output image is generated and the outputimage with a high resolution is obtained by performing image processingon an input image with a low resolution.

Furthermore, there are mainly two kinds of imaging apparatuses that usethe image sensor. One is a one-chip apparatus (hereinafter referred toas a one-chip camera) that uses one image sensor. The other is athree-chip apparatus (hereinafter referred to as a three-chip camera)that uses three image sensors.

In the three-chip camera, for example, three image sensors are used, onefor an R signal, one for a G signal, and one for a B signal, and thusthree primary color signals are obtained by the three image sensors.Then, a color image signal that is generated from the three primarycolor signals are stored in a recording medium.

In the one-chip camera, one image sensor is used in which a color codingfilter made from a color filter array assigned to every one pixel isinstalled in the front, and a signal of a color component that iscolor-coded by the color coding filter is obtained for every one pixel.As the color filter array that makes up the color coding filter, forexample, primary color filter arrays for red (R), green (G), and blue(B) or complementary filter arrays for yellow (Ye), cyan (Cy), andmagenta (Ma) are used. Then, in the one-chip camera, a signal of onecolor component is obtained for one pixel by the image sensor, a colorsignal other than the signal of the color component retained by eachpixel is generated by performing linear interpolation processing, andthus an image close to an image that is obtained by the three-chipcamera is obtained. In a video camera, a one-chip method is employed forminiaturization and weight saving.

As the color filter array that makes up the color coding filter, thecolor filter array in a Bayer layout is used most of the time. In theBayer layout, G color filters are arranged in a checkered pattern andR's and B's are alternately arranged in every line on the remainingportion.

In this case, in the image sensor, from each pixel in which a filter forone color among three primary colors, R, G, and B is arranged, only animage signal corresponding to such a filter color is output. That is,from the pixel in which an R color filter is arranged, an image signalof an R component is output, but image signals for a G component and a Bcomponent are not output. In the same manner, from a G pixel, only animage signal of the G component is output and the image signals for theR component and the B component are not output. From a B pixel, only theimage signal of the B component is output and the image signals for theR component and the G component are not output.

However, the image signals of the R component, the G component and the Bcomponent are necessary at the time of the processing of the signal ofeach pixel on the downstream side of the image processing. Accordingly,in the technology in the related art, the image signal of n×m R pixels,the image signal of n×m G pixels, and the image signal of n×m B pixelsare obtained, by their respective interpolation calculations, from anoutput from the image sensor that is configured from n×m (n and m arepositive integers) pixels, and are output to the downstream side.

Furthermore, a technology is proposed in which the image signal of 2n×2mR pixels is obtained, by the interpolation calculation, from the imagesignal of n×m R pixels, the image signal of 2n×2m G pixels is obtained,by the interpolation calculation, from the image signal of n×m G pixels,and the image signal of 2n×2m B pixels is obtained, by the interpolationcalculation, from the image signal of n×m B pixels (for example, referto Japanese Unexamined Patent Application Publication No. 2000-341705).

In Japanese Unexamined Patent Application Publication No. 2000-341705,pixel values for the pixel corresponding to an observation pixel and forthe vicinity thereof are set to be variables in an input image, and thepixel value for the observation pixel of an output pixel is predicted bya product and sum calculation that uses a coefficient that is obtainedby prior learning. By doing this, the three primary color signals can begenerated from an output from the image sensor of the one-chip camera,and an image signal with pixel density four times that of an originalimage can be generated.

SUMMARY

Incidentally, in Japanese Unexamined Patent Application Publication No.2000-341705, the pixel value, as is, corresponding to each of R, G, andB in the image sensor is used as a tap, a variable of predictioncalculation.

However, because a correlation among the pixel values of R, G, and B isoriginally low, for example, even though multiple pixel values for thevicinity of the observation pixel are input as the tap, it is difficultto produce a sufficient effect in the prediction calculation.

Furthermore, in the image sensor of the one-chip camera, in order toavoid the influence of a false color, an artifact or the like, lightincident on the image sensor generally is set to pass through an opticallow pass filter.

However, the image fades by setting the light incident on the imagesensor to pass through the optical low pass filter in this manner.

That is, in the technology in the related art, it is difficult to obtainthe three primary color signals without causing image degradation, suchas the fading of the image, the false color, or the artifact, in theone-chip camera.

Under this condition, it is difficult to achieve the sufficient effectbecause also in the image processing that obtains the output image witha high resolution from the input image with a low resolution, the imagedegradation, such as the false color or the artifact, that occurs at thetime of the Bayer conversion is emphasized in the technology in therelated art.

It is desirable to obtain an image signal of each color component froman output from an image sensor having a color filter array, that isconfigured from multiple color components, without degrading imagequality, when image processing is performed for resolution conversion.

According to an embodiment of the present technology, there is providedan image processing apparatus including a representative valuecalculation unit that selects a designation area, that is an area whichis configured from a predetermined number of pixels, from a first imagewhich is configured by using an image signal which is output from aone-chip pixel unit in which pixels corresponding to each colorcomponent in multiple color components, are regularly arranged on aplane, and that calculates a representative value of each of the colorcomponents in the designation area; a class classification unit thatperforms class classification on the designation area, based on anamount of characteristics that are obtained from a pixel value of thedesignation area; a coefficient reading unit that reads a coefficientthat is stored in advance, based on a result of performing the classclassification; a color component conversion unit that sets the pixelvalue relating to a predetermined pixel within the designation area tobe a prediction tap, sets the pixel value of one color component, amongthe multiple color components, to be a reference, and converts the pixelvalue of each color component of the prediction tap into a conversionvalue that is obtained by performing offset using the representativevalue; and a product and sum calculation unit that sets the conversionvalue to be a variable and calculates each of the pixel values of asecond image which is configured from only the pixels corresponding toeach color component in the multiple color components and which is animage different in resolution from the first image, by performingproduct and sum calculation which uses the coefficient which is read.

In the image processing apparatus, the one-chip pixel unit may be apixel unit that has R, G, and B color components, and the representativevalue calculation unit may calculate an interpolation value g of the Ror B pixel, based on the G pixel in the vicinity of the R or B pixel,may calculate an interpolation value r and an interpolation value b ofeach of the G pixels, based on the R pixel or the B pixel in thevicinity of the G pixel, may calculate the representative value of G byusing an average value of an input value G obtained directly from the Gpixel and the interpolation value g, may calculate the representativevalue of R, based on a difference between the interpolation value r andthe input value G and a difference between the input value R directlyobtained from the R pixel and the interpolation value g, and therepresentative value of the G, and may calculate the representativevalue of B, based on a difference between the interpolation value b andthe input value G and a difference between the input value B obtaineddirectly from the B pixel and interpolation value g, and therepresentative value of the G.

In the image processing apparatus, if the second image is an image thatis configured from only the G pixels, the color component conversionunit may offset the input value R by using a difference between therepresentative value of the R and the representative value of the G, andmay offset the input value B by using a difference between therepresentative value of the B and the representative value of the G; ifthe second image is an image that is configured from only the R pixels,the color component conversion unit may offset the input value G byusing a difference between the representative value of the G and therepresentative value of the R, and may offset the input value B by usinga difference between the representative value of the B and therepresentative value of the R; and if the second image is an image thatis configured from only the B pixels, the color component conversionunit may offset the input value G by using a difference between therepresentative value of the G and the representative value of the B, andmay offset the input value R by using a difference between therepresentative value of the R and the representative value of the B.

In the image processing apparatus, the one-chip image unit may be set tobe a pixel unit in an oblique Bayer layout in which the pixels in theBayer layout are obliquely arranged.

In the image processing apparatus, if the second image that isconfigured from only first color components is generated, among theimages with the multiple color components, and the second image that isconfigured from only second color components different from the firstcolor components may be generated, among the images with the multiplecolor components, the prediction tap may be acquired from the secondimage that is configured from only the first color components.

The image processing apparatus may further include a virtual colordifference calculation unit that calculates a virtual color differenceof the prediction tap, in which if the second image that is configuredfrom only the second color components different from the first colorcomponents is generated among the images with the multiple colorcomponents, the product and sum calculation unit may set the virtualcolor difference of the prediction tap to be the variable, may calculatethe virtual color difference of the second image by performing theproduct and sum calculation that uses the coefficient that is read, andthe prediction tap that is configured from only the pixels correspondingto the second color component may be acquired from the designation areain the first image.

In the image processing apparatus, the virtual color differencecalculation unit may calculate the virtual color difference bymultiplying the value of the pixel that makes up the prediction tap by amatrix coefficient that is stipulated by specification for color space.

The image processing apparatus may further include a different colorcomponent conversion unit that sets the pixel value relating to apredetermined pixel within the designation area to be a class tap, setsthe pixel value of one color component, among the multiple colorcomponents, to be a reference, and converts the pixel value of eachcolor component of the class tap into a conversion value that isobtained by performing offset using the representative value, in whichthe class classification unit may determine an amount of characteristicsof the class tap, based on the conversion value that results from theconversion by the different color component conversion unit.

In the image processing apparatus, the coefficient that is read by thecoefficient reading unit may be obtained by prior learning; in the priorlearning, the image that is configured by using each of the imagesignals that are output from the multiple pixel units, which arearranged in a position near a photographic subject, and each of which isconfigured from only the pixels corresponding to each of the multiplecolor components may be set to be a teacher image by using an opticallow pass filter that is arranged between the one-chip pixel unit and thephotographic subject; the image that is configured by using the imagesignal that is output from the one-chip pixel unit may be set to be astudent image; and the coefficient may be calculated by solving a normalequation in which the pixel of the student image and the pixel of theteacher image are mapped to each other.

According to another embodiment of the present technology, there isprovided an image processing method including enabling a representativevalue calculation unit to select a designation area that is an areawhich is configured from a predetermined number of pixels, from a firstimage which is configured by using an image signal which is output froma one-chip pixel unit in which pixels corresponding to each colorcomponent in multiple color components are regularly arranged on aplane, and to calculate a representative value of each of the colorcomponents in the designation area; enabling a class classification unitto perform class classification on the designation area, based on anamount of characteristics that are obtained from a pixel value of thedesignation area; enabling a coefficient reading unit to read acoefficient that is stored in advance, based on a result of performingthe class classification; enabling a color component conversion unit toset the pixel value relating to a predetermined pixel within thedesignation area to be a prediction tap, to set the pixel value of onecolor component, among the multiple color components, to be a reference,and to convert the pixel value of each color component of the predictiontap into a conversion value that is obtained by performing offset usingthe representative value; and enabling a product and sum calculationunit to set the conversion value to be a variable and to calculate eachof the pixel values of a second image which is configured from only thepixels corresponding to each color component in the multiple colorcomponents and which is an image different in resolution from the firstimage, by performing product and sum calculation which uses thecoefficient which is read.

According to still another embodiment of the present technology, thereis provided a program for causing a computer to function as an imageprocessing apparatus including a representative value calculation unitthat selects a designation area that is an area which is configured froma predetermined number of pixels, from a first image which is configuredby using an image signal which is output from a one-chip pixel unit inwhich pixels corresponding to each color component in multiple colorcomponents are regularly arranged on a plane, and that calculates arepresentative value of each of the color components in the designationarea; a class classification unit that performs class classification onthe designation area, based on an amount of characteristics that areobtained from a pixel value of the designation area; a coefficientreading unit that reads a coefficient that is stored in advance, basedon a result of performing the class classification; a color componentconversion unit that sets the pixel value relating to a predeterminedpixel within the designation area to be a prediction tap, sets the pixelvalue of one color component, among the multiple color components, to bea reference, and converts the pixel value of each color component of theprediction tap into a conversion value that is obtained by performingoffset using the representative value; and a product and sum calculationunit that sets the conversion value to be a variable and calculates eachof the pixel values of a second image which is configured from only thepixels corresponding to each color component in the multiple colorcomponents and which is an image which is different in resolution fromthe first image, by performing product and sum calculation which usesthe coefficient which is read.

According to the embodiments of the present technology, a designationarea, an area which is configured from a predetermined number of pixels,is selected from a first image which is configured by using an imagesignal which is output from a one-chip pixel unit in which pixelscorresponding to each color component in multiple color components areregularly arranged on a plane, and a representative value of each of thecolor components in the designation area is calculated; classclassification is performed on the designation area, based on an amountof characteristics that are obtained from a pixel value of thedesignation area; a coefficient that is stored in advance is read basedon a result of performing the class classification; the pixel valuerelating to a predetermined pixel within the designation area is set tobe a prediction tap, the pixel value of one color component, among themultiple color components, is set to be a reference, and the pixel valueof each color component of the prediction tap is converted into aconversion value that is obtained by performing offset using therepresentative value; and the conversion value is set to be a variableand each of the pixel values of a second image which is configured fromonly the pixels corresponding to each color component in the multiplecolor components and which is an image which is different in resolutionfrom the first image, is calculated by performing product and sumcalculation which uses the coefficient which is read.

According to the present technology, an image signal of each componentcan be obtained from an output from an image sensor having a colorfilter array that is configured from multiple color components, withoutdegrading image quality, when image processing is performed forresolution conversion.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram for describing a method of acquiring an image signalin an image sensor of a one-chip camera;

FIG. 2 is a block diagram illustrating a configuration example accordingto one embodiment of an image processing apparatus to which the presenttechnology is applied;

FIG. 3 is a diagram illustrating an example of a designation area;

FIG. 4 is a diagram for describing an example of a method of calculatingan interpolation value g;

FIG. 5 is a diagram for describing an example of a method of calculatingan interpolation value r;

FIG. 6 is a diagram for describing an example of a method of calculatingan interpolation value b;

FIG. 7 is a block diagram illustrating a configuration example of alearning apparatus corresponding to the image processing apparatus inFIG. 2;

FIGS. 8A to 8D are diagrams, each illustrating an example of a structureof a class tap or a prediction tap that is acquired in the imageprocessing apparatus in FIG. 2 or the learning apparatus in FIG. 7;

FIG. 9 is a flow chart describing an example of image processing by theimage processing apparatus in FIG. 2;

FIG. 10 is a flow chart describing an example of representative RGBcalculation processing;

FIG. 11 is a flow chart describing an example of coefficient learningprocessing by the learning apparatus in FIG. 7;

FIG. 12 is a block diagram illustrating a configuration example of animage processing apparatus according to another embodiment, to which thepresent technology is applied;

FIGS. 13A to 13D are diagrams, each illustrating an example of astructure of a class tap or a prediction tap that is acquired in theimage processing apparatus in FIG. 12;

FIGS. 14A to 14D are diagrams, each illustrating an example of thestructure of the class tap or the prediction tap that is acquired in theimage processing apparatus in FIG. 12;

FIGS. 15A to 15D are diagrams, each illustrating an example of thestructure of the class tap or the prediction tap that is acquired in theimage processing apparatus in FIG. 12;

FIG. 16 is a block diagram illustrating a configuration example of animage processing apparatus according to another embodiment, to which thepresent technology is applied;

FIGS. 17A to 17D are diagrams, each illustrating an example of thestructure of the class tap or the prediction tap that is acquired in theimage processing apparatus in FIG. 16;

FIGS. 18A to 18D are diagrams, each illustrating an example of thestructure of the class tap or the prediction tap that is acquired in theimage processing apparatus in FIG. 12;

FIGS. 19A to 19D are diagrams for describing the class tap or theprediction tap in a case of converting a resolution of an imageconfigured from pixels in a Bayer layout in the image processingapparatus in FIG. 2;

FIG. 20 is a diagram for describing an arrangement of the pixels in theBayer layout;

FIG. 21 is a diagram for describing an arrangement of the pixels in anoblique Bayer layout;

FIGS. 22A and 22B are diagrams for describing the class tap or theprediction tap in a case of converting a resolution of an imageconfigured from the pixels in the oblique Bayer layout in the imageprocessing apparatus in FIG. 2;

FIGS. 23A to 23D are diagrams for describing an example of the class tapor the prediction tap in the case of converting the resolution of theimage configured from the pixels in the Bayer layout in the imageprocessing apparatus in FIG. 12;

FIGS. 24A and 24B are diagrams for describing an example of the classtap or the prediction tap in the case of converting the resolution ofthe image configured from the pixels in the oblique Bayer layout in theimage processing apparatus in FIG. 12;

FIGS. 25A to 25D are diagrams for describing the class tap or theprediction tap in the case of converting the resolution of the imageconfigured from the pixels in the Bayer layout in the image processingapparatus in FIG. 16;

FIGS. 26A to 26D are diagrams for describing the class tap or theprediction tap in the case of converting the resolution of the imageconfigured from the pixels in the Bayer layout in the image processingapparatus in FIG. 16;

FIGS. 27A to 27D are diagrams for describing the class tap or theprediction tap in the case of converting the resolution of the imageconfigured from the pixels in the Bayer layout in the image processingapparatus in FIG. 16;

FIGS. 28A and 28B are diagrams for describing an example of the classtap or the prediction tap in the case of converting the resolution ofthe image configured from the pixels in the oblique Bayer layout in theimage processing apparatus in FIG. 16;

FIGS. 29A and 29B are diagrams for describing an example of the classtap or the prediction tap in the case of converting the resolution ofthe image configured from the pixels in the oblique Bayer layout in theimage processing apparatus in FIG. 16; and

FIG. 30 is a block diagram illustrating a configuration example of apersonal computer.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the technology disclosed in the present specification aredescribed below referring to the drawings.

FIG. 1 is a diagram for describing a method of acquiring an image signalin a one-chip camera image sensor.

In this example, light reflected from a photographic subject 11 passesthrough an optical low pass filter 12 and is received by an image sensor13.

In the one-chip camera, one image sensor is used in which a color codingfilter made from a color filter array assigned to every one pixel isinstalled in the front, and a signal of a color component that iscolor-coded by the color coding filter is obtained for every one pixel.

At this point, the color filter array in a Bayer layout is used in theimage sensor 13, G color filters are arranged in a checkered pattern,and R's and B's are alternately arranged in every line on the remainingportion. That is, the four pixels within a rectangular region in theimage sensor 13 are configured from two G pixels, and one R pixel andone B pixel.

In one-chip camera, image signals for an R component, a G component anda B component are necessary for every pixel at the time of theprocessing of a signal of each pixel on the downstream side of imageprocessing. Because of this, it is necessary to obtain the pixel valuesfor the R component, the G component and the B component for every pixelby performing an interpolation operation, based on a pixel value that isoutput from the image sensor 13.

Furthermore, in the image sensor 13, in order to avoid the influence ofa false color, an artifact or the like, light incident on the imagesensor is set to pass through the optical low pass filter 12. However,an image fades by setting the light incident on the image sensor to passthrough the optical low pass filter 12 in this manner.

Accordingly, according to the present technology, it is possible toobtain the pixel value that is obtained when three of the image sensorscorresponding to the R component, the G component, and the B component,respectively, are arranged in what appears like a frame (a rectangleindicated by a dotted line in the drawing) 14, based on the pixel valuethat is output from the image sensor 13.

FIG. 2 is a block diagram illustrating a configuration example accordingto one embodiment of an image processing device to which the presenttechnology is applied. In the image processing apparatus 100, the pixelvalues for the pixel corresponding to an observation pixel and for thevicinity thereof are set to be variables in an input image, and thepixel value for the observation pixel of an output image is predicted bya product and sum calculation that uses a coefficient that is obtainedby prior learning.

The input image that is input into the image processing apparatus 100,for example, is set to be an image configured with an output value forthe image sensor in which the color filter array in a Bayer layout isused. That is, the input image is set to be an image corresponding to asignal that is input from the image sensor 13 in FIG. 1. Therefore, inthe input image, the image signal of the R component is obtained fromthe pixel on which an R color filter is arranged, but the image signalsof the G component and the B component are not obtained. In the samemanner, only the image signal of the G component is obtained from a Gpixel, the image signals of the R component and the B component are notobtained. Then, only the image signal of the B component is obtainedfrom a B pixel and the image signals of the R component and the Gcomponent are not obtained.

The image processing apparatus 100 in FIG. 2 is configured from arepresentative RGB calculation unit 101, and class tap selection unitsthat correspond to RGB colors, respectively, prediction tap selectionunits that correspond to the RGB colors, respectively, color conversionunits that correspond to the RGB colors, respectively, classclassification units that correspond to the RGB colors, respectively,coefficient memories that correspond to the RGB colors, respectively,and product and sum calculation units that correspond to the RGB colors,respectively.

In a region (referred to as a designation area) in the image foracquiring a class tap or a prediction tap described below, therepresentative RGB calculation unit 101 calculates Dr, Db, and Dg asrepresentative values that are set to be references for the pixel valuesof the color components of R, G, and B, respectively.

For example, as indicated by a thick-line frame in FIG. 3, thedesignation area is set. In FIG. 3, each circle in the drawingrepresents the pixel of the input image and the pixel indicated by ahatched circle in the center is set to be a central pixel of the classtap or the prediction tap. Moreover, letters R, G, B, each of which iswritten within each circle, express the color component of each pixel.

The designation area, a region that includes the class tap or theprediction tap with the central pixel being set to be the center, isarbitrarily set, but when the designation area is set to be a regionthat largely exceeds the class tap or the prediction tap, optimalprocessing according to the region of the image is difficult to perform.Because of this, it is preferable that the designation area be set to bethe same as the class tap or the prediction tap.

Moreover, in the following description, an average value, aninterpolation value, a representative value, and the like that arecalculated by an operation are properly referred to, but the pixelvalues of the pre-operation input image are referred to as an inputvalue G, an input value R, and an input value B, respectively, accordingto the color component of each pixel in order to distinguish among thepixel values. That is, the pixel value that is obtained directly fromthe pixel in which the R color filter of the image sensor in the Bayerlayout is arranged is set to be the input value R, the pixel value thatis obtained directly from the pixel in which the G color filter of theimage sensor in the Bayer layout is arranged is set to be the inputvalue G, and the pixel value that is obtained directly from the pixel inwhich the B color filter of the image sensor in the Bayer layout isarranged is set to be the input value B.

In this example, the region that is surrounded by the thick line in thedrawing and that is configured from 25 (=5×5) pixels with the centralpixel being set to be the center is set to be the designation area.

First, the representative RGB calculation unit 101 calculates therepresentative value Dg of the G component.

At this time, the representative RGB calculation unit 101, asillustrated in FIG. 4, averages an input value G1 to an input value G4of a pixel G1 to a pixel G4, the four G pixels in the vicinity (in theupward, downward, leftward, and rightward directions) of the centralpixel, with an R component pixel or a B component pixel with thedesignation area being set to be the center, and thus calculates theinterpolation value g that is a value of the interpolated G component ina pixel position of the central pixel. By doing this, the R componentpixel and the B component pixel that do not have the G component in theinput image have the interpolated G component (the interpolation valueg).

Then, the representative RGB calculation unit 101 calculates as therepresentative value Dg the average value of the input values G of allthe G pixels (here, 12 pieces) within the designation area and theinterpolation value g.

Next, the representative RGB calculation unit 101 calculates therepresentative value Dr of the R component. At this time, therepresentative RGB calculation unit 101 calculates the interpolationvalue r that is a value of the interpolated R component in each pixelposition of the G pixels within the designation area. For example, ifthe interpolation value r in the position indicated by the pixel Cl orthe pixel G4 in FIG. 4 is calculated, as illustrated in FIG. 5, theaverage value of a pixel R1 and a pixel R2 that are positionedimmediately to the left of the G pixel and immediately to the right ofthe G pixel, respectively, is set to be the interpolation value r.

By doing this, the input value G and the interpolation value r can beobtained in the pixel position of the G pixel within the designationarea, and the input value R and the interpolation value g can beobtained in the pixel position of the R pixel within the designationarea.

Then, in each pixel position, (the interpolation value r−the input valueG) and (the input value R−the interpolation value g) are calculated, andthe representative value Dr is calculated as a value that results fromadding the representative value Dg to the average value of calculated(the interpolation value r−the input value G) and (the input value R−theinterpolation value g).

Additionally, the representative RGB calculation unit 101 calculates therepresentative value Db of the B component. At this time, therepresentative RGB calculation unit 101 calculates an interpolationvalue b that is a value of the interpolated B component in each pixelposition of the G pixels within the designation area. For example, ifthe interpolation value b in the position indicated by the pixel G1 orthe pixel G4 in FIG. 4 is calculated, as illustrated in FIG. 6, theaverage value of a pixel B1 and a pixel B2 that are positionedimmediately over the G pixel and immediately under the G pixel,respectively, is set to be the interpolation value b.

By doing this, the input value G and the interpolation value b can beobtained in the pixel position of the G pixel within the designationarea, and the input value B and the interpolation value g can beobtained in the pixel position of the B pixel within the designationarea.

Then, in each pixel position, (the interpolation value b−the input valueG) and (the input value B−the interpolation value g) are calculated, andthe representative value Db is calculated as a value that results fromadding the representative value Dg to the average value of thecalculated (the interpolation value b−the input value G) and thecalculated (the input value B−the interpolation value g).

Referring back to FIG. 2, a G class tap selection unit 102-1 selectsfrom the input image a G class tap that is a class tap necessary forgenerating a G component image and acquires the G class tap. The G classtap, for example, is configured from a predetermined number of pixels inwhich the pixel of the input image in the position corresponding to theobservation pixel of an output image is set to be the central pixel andthe central pixel is set to be the center.

The G class tap selected by the G class tap selection unit 102-1 issupplied to a G conversion unit 105-11. The G conversion unit 105-11 isset to perform G conversion processing on each pixel value that makes upthe G class tap.

The G conversion processing, for example, is performed as follows. Ifthe pixel value that makes up the G class tap is the input value G, aconversion value G′ is calculated, if the pixel value that makes up theG class tap is the input value R, a conversion value R′ is calculated,and if the pixel value that makes up the G class tap is the input valueB, a conversion value B′ is calculated.

At this point, the conversion value G′, the conversion value R′, and theconversion value B′ are calculated using Equations (1) to (3).

G′=G  (1)

R′=R−(Dr−Dg)  (2)

B′=B−(Db−Dg)  (3)

Correlation between the pixel values, each of which makes up the G classtap, is increased by performing the G conversion processing. That is,the pixel value of each of the R pixel and the B pixel of the inputimage is offset with the pixel value of the G pixel being set to be thereference, and thus a change due to a difference in color componentbetween the pixel values, each of which makes up the G class tap can beremoved.

Referring back to FIG. 2, the G class tap that is output from the Gconversion unit 105-11 is supplied to a G class classification unit106-1. Moreover, the G class tap that is output from the G conversionunit 105-11 is configured from the conversion value G′, the conversionvalue R′, and the conversion value B′ that are calculated usingEquations (1) to (3) described above.

The G class classification unit 106-1 codes the supplied G class tapusing adaptive dynamic range coding (ADRC), and thus generates a classcode. The class code generated here is output to a G coefficient memory107-1.

The G coefficient memory 107-1 reads the coefficient that is stored withit being mapped to the class code that is output from the G classclassification unit 106-1. Then, the G coefficient memory 107-1 suppliesthe read coefficient to a G product and sum calculation unit 108-1.Moreover, the coefficient that is a coefficient obtained by priorlearning and that is used in the product and sum calculation describedbelow is stored in the G coefficient memory 107-1 with it being mappedto the class code.

A G prediction tap selection unit 103-1 selects from the input image a Gprediction tap that is a prediction tap necessary for generating the Gcomponent image and acquires the G prediction tap. The G prediction tap,for example, is configured from a predetermined number of pixels inwhich the pixel of the input image in the position corresponding to theobservation pixel of the output image is set to be the central pixel andthe central pixel is set to be the center.

The G prediction tap selected by the G prediction tap selection unit103-1 is supplied to a G conversion unit 105-12. The G conversion unit105-12 is set to perform the G conversion processing on each pixel valuethat makes up the G prediction tap.

The G conversion processing by the G conversion unit 105-12 is the sameas that by the G conversion unit 105-11. That is, using Equations (1) to(3) described above, if the pixel value that makes up the G predictiontap is the input value G, the conversion value G′ is calculated, if thepixel value that makes up the G prediction tap is the input value R, theconversion value R′ is calculated, and if the pixel value that makes upthe G prediction tap is the input value 13, the conversion value 3′ iscalculated.

The G prediction tap that is output from the G conversion unit 105-12 issupplied to the G product and sum calculation unit 108-1. Moreover, theG prediction tap that is output from the G conversion unit 105-12 isconfigured from the conversion value G′, the conversion value R′, andthe conversion value B′ that are calculated using Equations (1) to (3)described above.

The G product and sum calculation unit 108-1 substitutes the Gprediction tap, as the variable, which is output from the G conversionunit 105-12 in a pre-constructed linear simple equation, and performs aprediction value operation using the coefficient that is supplied fromthe G coefficient memory 107-1. That is, the G product and sumcalculation unit 108-1 performs prediction calculation on the pixelvalue of the observation pixel in the G component image (referred to asa G output image) that becomes the output image, based on the Gprediction tap.

At this point, the prediction calculation of the pixel value of theobservation pixel of the output image is described.

Now, for example, image data that is output from the image sensor thathas the color filter array in the Bayer layout is defined as first imagedata, and the image data that is output from the G component imagesensor that is arranged in a frame 14 in FIG. 1 is defined as secondimage data. Then, it is considered that the pixel values of the secondimage data are obtained from the pixel values of the first image datausing predetermined prediction calculation.

When for example, linear simple prediction calculation, as thepredetermined prediction calculation, is set to be employed, a pixelvalue y of the pixel of the second image data (hereinafter suitablyreferred to as the pixel of the second image) is obtained using thefollowing linear simple expression.

$\begin{matrix}{y = {\sum\limits_{n = 1}^{N}\; {w_{n}x_{n}}}} & (4)\end{matrix}$

However, in Equation (4), x_(n) indicates the pixel value of the pixel(hereinafter properly referred to as the pixel of a first image) of n-thfirst image data, which makes up the prediction tap for a pixel y of asecond image, and w_(n) indicates an n-th tap coefficient that ismultiplied by the pixel (the pixel value of) of an n-th first image.Moreover, in Equation (4), the prediction tap is configured from the npixels x₁, x₂, and so forth up to x_(N) of the first image.

At this point, the pixel value y of the pixel of the second image can beobtained also by a quadratic or higher equation, not the linear simpleequation expressed in Equation (4).

Now, when a true value of the pixel value of the pixel of the secondimage in a k-th sample is indicated by y_(k) and a prediction value ofthe true value y_(k) thereof that is obtained by Equation (4) is y_(k)′,a prediction error e_(k) thereof is expressed by the following equation.

e _(k) =y _(k) −y _(k)′  (5)

Now, because the prediction value y_(k)′ in Equation (5) is obtainedaccording to Equation (4), when y_(k)′ in Equation (5) is replacedaccording to Equation (4), the following equation is obtained.

$\begin{matrix}{e_{k} = {y_{k} - ( {{\sum\limits_{n = 1}^{N}\; {w_{n}x_{n}}},k} )}} & (6)\end{matrix}$

However, in Equation (6), x_(n,k) indicates the pixel of the n-th firstimage that makes up the prediction tap for the pixel of the second imagein the k-th sample.

A tap coefficient w_(n) that sets the prediction error e_(k) in Equation(6) (or Equation (5)) to be 0 is optimal when predicting the pixel ofthe second image, but generally it is difficult to obtain the tapcoefficient w_(n) for all pixels of all the second images.

Accordingly, when a least-squares method, for example, is employed as astandard for indicating that the tap coefficient w_(n) is optimal, theoptimal tap coefficient w_(n) can be obtained by minimizing a sum totalE of square errors that is expressed by the following equation.

$\begin{matrix}{E = {\sum\limits_{k = 1}^{K}\; e_{k}^{2}}} & (7)\end{matrix}$

However, in Equation (7), K indicates the number (the number of learningsamples) of samples of sets of a pixel y_(k) of the second image andpixels x_(1,k), x_(2,k), and so forth up to x_(N,k) of the first image,each of which makes up the prediction tap for the pixel y_(k) of thesecond image.

A minimum value (the smallest value) of the sum total E of the squareerror in Equation (7), as illustrated in Equation (8), is given by w_(n)that sets a result of partially differentiating the sum total E with thetap coefficient w_(n) to be 0.

$\begin{matrix}{\frac{\partial E}{\partial w_{n}} = {{{e_{1}\frac{\partial e_{1}}{\partial w_{n}}} + {e_{2}\frac{\partial e_{2}}{\partial w_{n}}} + \ldots + {e_{k}\frac{\partial e_{k}}{\partial w_{n}}}} = {0\mspace{14mu} ( {{n = 1},2,\ldots \mspace{14mu},N} )}}} & (8)\end{matrix}$

Accordingly, when Equation (6) described above is partiallydifferentiated with the tap coefficient w_(n) the following equation isobtained.

$\begin{matrix}{{\frac{\partial e_{k}}{\partial w_{1}} = {- x_{1,k}}},{\frac{\partial e_{k}}{\partial w_{2}} = {- x_{2,k}}},\ldots \mspace{14mu},{\frac{\partial e_{k}}{\partial w_{N}} = {- x_{N,k}}},\mspace{14mu} ( {{k = 1},2,\ldots \mspace{14mu},K} )} & (9)\end{matrix}$

The following equation is obtained from Equations (8) and (9).

$\begin{matrix}{{\sum\limits_{k = 1}^{K}\; {e_{k}x_{1}}},{k = 0},{\sum\limits_{k = 1}^{K}\; {e_{k}x_{2}}},{k = 0},{\ldots \mspace{14mu} {\sum\limits_{k = 1}^{K}\; {e_{k}x_{N}}}},{k = 0}} & (10)\end{matrix}$

Equation (10) can be expressed by a normal equation expressed inEquation (11) when Equation (6) is substituted for e_(k) in Equation(10).

$\begin{matrix}{\begin{bmatrix}( {\sum\limits_{k = 1}^{K}\; {x_{1\;,k}x_{1,k}}} ) & ( {\sum\limits_{k = 1}^{K}\; {x_{1\;,k}x_{2,k}}} ) & \ldots & ( {\sum\limits_{k = 1}^{K}\; {x_{1\;,k}x_{N,k}}} ) \\( {\sum\limits_{k = 1}^{K}\; {x_{2\;,k}x_{1,k}}} ) & ( {\sum\limits_{k = 1}^{K}\; {x_{2\;,k}x_{2,k}}} ) & \ldots & ( {\sum\limits_{k = 1}^{K}\; {x_{2\;,k}x_{N,k}}} ) \\\vdots & \vdots & \ddots & \vdots \\( {\sum\limits_{k = 1}^{K}\; {x_{N\;,k}x_{1,k}}} ) & ( {\sum\limits_{k = 1}^{K}\; {x_{N\;,k}x_{2,k}}} ) & \ldots & ( {\sum\limits_{k = 1}^{K}\; {x_{N\;,k}x_{N,k}}} )\end{bmatrix}{\quad{\begin{bmatrix}w_{1} \\\; \\w_{2} \\\vdots \\w_{N}\end{bmatrix} = \begin{bmatrix}( {\sum\limits_{k = 1}^{K}\; {x_{1\;,k}y_{k}}} ) \\( {\sum\limits_{k = 1}^{K}\; {x_{2\;,k}y_{k}}} ) \\\vdots \\( {\sum\limits_{k = 1}^{K}\; {x_{N\;,k}y_{k}}} )\end{bmatrix}}}} & (11)\end{matrix}$

The normal equation in Equation (11), for example, can be solved for thetap coefficient w_(n) using a sweep-out method (a Gauss-Jordanelimination method) and the like.

The optimal tap coefficient w_(n) (here, the tap coefficient thatminimizes the sum total E of the square error) can be obtained for everyclass by making and solving the normal equation in Equation (11) forevery class. For example, the tap coefficient w_(n) that is obtained inthis manner is stored, as a G coefficient, in the G coefficient memory107-1. Moreover, a method of obtaining the coefficient by prior learningis described in detail below.

For example, the G prediction tap that goes through the processing bythe G conversion unit 105-12 is substituted for pixels x₁, x₂, and soforth up to x_(N) in Equation (4), the tap coefficient w_(n) in Equation(4) is supplied from the G coefficient memory 107-1, and the operationin Equation (4) is performed in the G product and sum calculation unit108-1. Thus, the pixel value of an observation image of the output imageis predicted.

In this manner, the G output image can be obtained by predicting eachobservation pixel.

An R class tap selection unit 102-2 selects from the input image an Rclass tap that is a class tap necessary for generating an R componentimage and acquires the R class tap. The R class tap, for example, isconfigured from a predetermined number of pixels in which the pixel ofthe input image in the position corresponding to the observation pixelof the output image is set to be the central pixel and the central pixelis set to be the center.

The R class tap selected by the R class tap selection unit 102-2 issupplied to an R conversion unit 105-21. The R conversion unit 105-21performs R conversion processing on each pixel value that makes up the Rclass tap.

The R conversion processing, for example, is performed as follows. Ifthe pixel value that makes up the R class tap is the input value G, theconversion value G′ is calculated, if the pixel value that makes up theR class tap is the input value R, the conversion value R′ is calculated,and if the pixel value that makes up the R class tap is the input valueB, the conversion value B′ is calculated.

At this point, the conversion value G′, the conversion value R′, and theconversion value B′ are calculated using Equations (12) to (14).

G′=G−(Dg−Dr)  (12)

R′=R  (13)

B′=B−(Db−Dr)  (14)

The correlation between the pixel values, each of which makes up the Rclass tap, is increased by performing the R conversion processing. Thatis, the pixel value of each of the G pixel and the B pixel of the inputimage is offset with the pixel value of the R pixel being set to be thereference, and thus the change due to the difference in color componentbetween the pixel values, each of which makes up the R class tap, can beremoved.

Referring back to FIG. 2, the R class tap that is output from the Rconversion unit 105-21 is supplied to an R class classification unit106-2. Moreover, the R class tap that is output from the R conversionunit 105-21 is configured from the conversion value G′, the conversionvalue R′, and the conversion value B′ that are calculated usingEquations (12) to (14) described above.

The R class classification unit 106-2 codes the supplied R class tap byperforming the adaptive dynamic range coding (ADRC), and thus generatesa class code. The class code generated here is output to the Rcoefficient memory 107-2.

The R coefficient memory 107-2 reads the coefficient that is stored withit being mapped to the class code that is output from the R classclassification unit 106-2. Then, the R coefficient memory 107-2 suppliesthe read coefficient to an R product and sum calculation unit 108-2.Moreover, the coefficient that is a coefficient obtained by priorlearning and that is used in the product and sum calculation describedbelow is stored in the R coefficient memory 107-2 with it being mappedto the class code.

An R prediction tap selection unit 103-2 selects from the input image anR prediction tap that is a prediction tap necessary for generating the Rcomponent image and acquires the R prediction tap. The R prediction tap,for example, is configured from a predetermined number of pixels inwhich the pixel of the input image in the position corresponding to theobservation pixel of the output image is set to be the central pixel andthe central pixel is set to be the center.

The R prediction tap selected by the R prediction tap selection unit103-2 is supplied to an R conversion unit 105-22. The R conversion unit105-22 performs the R conversion processing on each pixel value thatmakes up the R prediction tap.

The R conversion processing by the R conversion unit 105-22 is the sameas that by the R conversion unit 105-21. That is, using Equations (12)to (14) described above, if the pixel value that makes up the Rprediction tap is the input value G, the conversion value G′ iscalculated, if the pixel value that makes up the R prediction tap is theinput value R, the conversion value R′ is calculated, and if the pixelvalue that makes up the R prediction tap is the input value B, theconversion value B′ is calculated.

The R prediction tap that is output from the R conversion unit 105-22 issupplied to the R product and sum calculation unit 108-2. Moreover, theR prediction tap that is output from the R conversion unit 105-21 isconfigured from the conversion value G′, the conversion value R′, andthe conversion value B′ that are calculated using Equations (12) to (14)described above.

The R product and sum calculation unit 108-2 substitutes the Rprediction tap, as the variable, which is output from the R conversionunit 105-22 in the pre-constructed linear simple equation, and performsthe prediction value operation using the coefficient that is suppliedfrom the R coefficient memory 107-2. That is, the R product and sumcalculation unit 108-2 performs the prediction calculation on the pixelvalue of the observation pixel in the R component image (referred to asan R output image) that becomes the output image, based on the Rprediction tap.

For example, the R prediction tap that goes through the processing bythe R conversion unit 105-22 is substituted for the pixels x₁, x₂, andso forth up to x_(N) in Equation (4), the tap coefficient w_(n) inEquation (4) is supplied from the R coefficient memory 107-2, and theoperation in Equation (4) is performed in the R product and sumcalculation unit 108-2. Thus, the pixel value of the observation imageof the output image is predicted.

In this manner, the R output image can be obtained by predicting eachobservation pixel.

A B class tap selection unit 102-3 selects from the input image a Bclass tap that is a class tap necessary for generating a B componentimage and acquires the B class tap. The B class tap, for example, isconfigured from a predetermined number of pixels in which the pixel ofthe input image in the position corresponding to the observation pixelof the output image is set to be the central pixel and the central pixelis set to be the center.

The B class tap selected by the B class tap selection unit 102-3 issupplied to a B conversion unit 105-31. The B conversion unit 105-31performs B conversion processing on each pixel value that makes up the Bclass tap.

The B conversion processing, for example, is performed as follows. Ifthe pixel value that makes up the B class tap is the input value G, theconversion value G′ is calculated, if the pixel value that makes up theB class tap is the input value R, the conversion value R′ is calculated,and if the pixel value that makes up the B class tap is the input valueB, the conversion value B′ is calculated.

At this point, the conversion value G′, the conversion value R′, and theconversion value B′ are calculated using Equations (15) to (17).

G′=G−(Dg−Db)  (15)

R′=R−(Dr−Db)  (16)

B′=B  (17)

The correlation between the pixel values, each of which makes up the Bclass tap, is increased by performing the B conversion processing. Thatis, the pixel value of each of the G pixel and the R pixel of the inputimage is offset with the pixel value of the B pixel being set to be thereference, and thus the change due to the difference in color componentbetween the pixel values, each of which makes up the B class tap can beremoved.

Referring back to FIG. 2, the B class tap that is output from the Bconversion unit 105-31 is supplied to a B class classification unit106-3. Moreover, the B class tap that is output from the B conversionunit 105-31 is configured from the conversion value G′, the conversionvalue R′, and the conversion value B′ that are calculated usingEquations (15) to (17) described above.

The B class classification unit 106-3 codes the supplied B class tap byperforming the adaptive dynamic range coding (ADRC), and thus generatesa class code. The class code generated here is output to the Bcoefficient memory 107-3.

The B coefficient memory 107-3 reads the coefficient that is stored withit being mapped to the class code that is output from the B classclassification unit 106-3. Then, the B coefficient memory 107-3 suppliesthe read coefficient to a B product and sum calculation unit 108-3.Moreover, the coefficient that is a coefficient obtained by priorlearning and that is used in the product and sum calculation describedbelow is stored in the B coefficient memory 107-3 with it being mappedto the class code.

A B prediction tap selection unit 103-3 selects from the input image a Bprediction tap that is a prediction tap necessary for generating the Bcomponent image and acquires the B prediction tap. The B prediction tap,for example, is configured from a predetermined number of pixels inwhich the pixel of the input image in the position corresponding to theobservation pixel of the output image is set to be the central pixel andthe central pixel is set to be the center.

The B prediction tap selected by the B prediction tap selection unit103-3 is supplied to a B conversion unit 105-32. The B conversion unit105-32 performs the B conversion processing on each pixel value thatmakes up the B prediction tap.

The B conversion processing by the B conversion unit 105-32 is the sameas that by the B conversion unit 105-31. That is, using Equations (15)to (17) described above, if the pixel value that makes up the Bprediction tap is the input value G, the conversion value G′ iscalculated, if the pixel value that makes up the B prediction tap is theinput value R, the conversion value R′ is calculated, and if the pixelvalue that makes up the B prediction tap is the input value B, theconversion value B′ is calculated.

The B prediction tap that is output from the B conversion unit 105-32 issupplied to the B product and sum calculation unit 108-3. Moreover, theB prediction tap that is output from the B conversion unit 105-31 isconfigured from the conversion value G′, the conversion value R′, andthe conversion value B′ that are calculated using Equations (15) to (17)described above.

The B product and sum calculation unit 108-3 substitutes the Bprediction tap, as the variable, which is output from the B conversionunit 105-32 in the pre-constructed linear simple equation, and performsthe prediction value operation using the coefficient that is suppliedfrom the B coefficient memory 107-3. That is, the B product and sumcalculation unit 108-3 performs prediction calculation on the pixelvalue of the observation pixel in the B component image (referred to asa B output image) that becomes the output image, based on the Bprediction tap.

For example, the B prediction tap that goes through the processing bythe B conversion unit 105-32 is substituted for the pixels x₁, x₂, andso forth up to x_(N) in Equation (4), the tap coefficient w_(n) inEquation (4) is supplied from the B coefficient memory 107-3, and theoperation in Equation (4) is performed in the B product and sumcalculation unit 108-3. Thus, the pixel value of the observation imageof the output image is predicted.

In this manner, the B output image can be obtained by predicting eachobservation pixel.

Next, the performing of the learning on the coefficient that is storedin the G coefficient memory 107-1, the R coefficient memory 107-2, andthe B coefficient memory 107-3 is described.

FIG. 7 is a block diagram illustrating a configuration example of alearning apparatus corresponding to the image processing apparatus 100in FIG. 2.

As illustrated in FIG. 7, a learning apparatus 200 includes anobservation pixel selection unit 201, a student image generation unit202, a representative RGB calculation unit 203, a class tap selectionunit 204, a prediction tap selection unit 205, a color conversion unit206-1, a color conversion unit 206-2, a class classification unit 207, anormal equation addition unit 208, and a coefficient data generationunit 209.

If the learning apparatus 200 performs the learning on the coefficients,for example, the G component image, the R component image, and the Bcomponent image, as teacher images that are obtained by arranging in theframe 14 in FIG. 1 the three image sensors that correspond to the Rcomponent, the G component, and the B component, respectively, areprepared.

For example, by using a simulation model for an optical low pass filterand so forth, the student image generation unit 202 degrades the teacherimage and generates the image that is output from the image sensor thatis configured from the pixels arranged according to the Bayer layout.The image that is generated in this manner is defined as a studentimage.

The observation pixel selection unit 201 selects one arbitrary pixel, asthe observation pixel, from the teacher images. Moreover, a coordinatevalue of the pixel selected as the observation pixel and the like areset in such a manner that they are supplied to the representative RGBcalculation unit 203, the class tap selection unit 204, and theprediction tap selection unit 205.

The representative ROB calculation unit 203 calculates a representativevalue Dg, a representative value Dr, and a representative value Db forthe pixel within the designation area in the student image, as is thecase with the representative RGB calculation unit 101 in FIG. 2.Moreover, the designation area is set to be a predetermined region inwhich the pixel in the position corresponding to the observation pixelselected by the observation pixel selection unit 201 is set to be thecenter.

The class tap selection unit 204 selects the class tap from the pixelswithin the designation area in the student image and acquires the classtap. Moreover, if the observation pixel selection unit 201 selects theobservation pixel from the G component image among the teacher images,the class tap selection unit 204 is set in such a manner that it selectsthe G class tap. Furthermore, if the observation pixel selection unit201 selects the observation pixel from the R component image among theteacher images, the class tap selection unit 204 is set to select the Rclass tap, and if the observation pixel selection unit 201 selects theobservation pixel from the B component image among the teacher images,the class tap selection unit 204 is set to select the B class tap.

The prediction tap selection unit 205 selects the prediction tap fromthe pixels within the designation area in the student image and acquiresthe prediction tap. Moreover, if the observation pixel selection unit201 selects the observation pixel from the G component image among theteacher images, the prediction tap selection unit 205 is set to selectthe G prediction tap. Furthermore, if the observation pixel selectionunit 201 selects the observation pixel from the R component image amongthe teacher images, the prediction tap selection unit 205 is set toselect the R prediction tap, and if the observation pixel selection unit201 selects the observation pixel from the B component image among theteacher images, the prediction tap selection unit 205 is set to selectthe B prediction tap.

The color conversion unit 206-1 performs predetermined conversionprocessing on the class tap that is acquired by the class tap selectionunit 204. At this point, if the G class tap is acquired by the class tapselection unit 204, the color conversion unit 206-1 is set to performthe G conversion processing. Furthermore, if the R class tap is acquiredby the class tap selection unit 204, the color conversion unit 206-1 isset to perform the R conversion processing, and if the B class tap isacquired by the class tap selection unit 204, the color conversion unit206-1 is set to perform the B conversion processing.

The class tap that goes through the processing by the color conversionunit 206-1 is supplied to the class classification unit 207.

The color conversion unit 206-2 performs a predetermined conversionprocessing on the prediction tap acquired by the prediction tapselection unit 205. At this point, if the G prediction tap is acquiredby the prediction tap selection unit 205, the color conversion unit206-2 is set to perform the G conversion processing. Furthermore, if theR prediction tap is acquired by the prediction tap selection unit 205,the color conversion unit 206-2 is set to perform the R conversionprocessing, and if the B prediction tap is acquired by the predictiontap selection unit 205, the color conversion unit 206-2 is set toperform the B conversion processing.

The prediction tap that goes through the processing by the colorconversion unit 206-2 is supplied to the normal equation addition unit208.

The class classification unit 207 codes the supplied class tap byperforming the adaptive dynamic range coding (ADRC), and thus generatesa class code. The class code generated here is supplied to the normalequation addition unit 208, along with the class tap.

The normal equation addition unit 208, for example, generates the linearsimple equation expressed in Equation (4). At this time, the class tapsthat go through the processing by the color conversion unit are used asthe pixels x₁, x₂ and so forth up to x_(N) in Equation (4).

When the observation pixel selection unit 201 selects a new observationpixel, a new linear simple equation is generated in the same manner asin the case above described. The normal equation addition unit 208 addsthe linear simple equation generated in this manner to every class codeand thus generates the normal equation in Equation (11).

The coefficient data generation unit 209 solves the normal equation inEquation (11) for the tap coefficient w_(n) by using the sweep-outmethod (the Gauss-Jordan elimination method) and the like. Then,according to a type of teacher image (the G component image, the Rcomponent image, or the B component image) in which the observationpixel is set, the coefficient data generation unit 209 outputs theobtained tap coefficient w_(n), as a G coefficient necessary forperforming the prediction calculation of the G output image, an Rcoefficient necessary for performing the prediction calculation of the Routput image, or a B coefficient necessary for performing the predictioncalculation of the B output image.

Thus, the G coefficient, the R coefficient, and the B coefficient forevery class code obtained are stored in the G coefficient memory 107-1,the R coefficient memory 107-2, and the B coefficient memory 107-3 inFIG. 2, respectively.

Thus, the learning is performed on the coefficient.

FIGS. 5A to 8D are diagrams, each illustrating an example of a structureof the class tap or the prediction tap that is acquired in the imageprocessing apparatus 100 in FIG. 2 or the learning apparatus 200 in FIG.7. At this point, the class tap refers collectively to the G class tap,the R class tap, and the B class tap that are described above, and theprediction tap refers collectively to the G prediction tap, the Rprediction tap, and the B prediction tap that are described above.

In each of the examples in FIGS. 5A to 8D, the class tap or theprediction tap that is configured from nine (=3×3) pixels in which thepixel (the central pixel) of the input image corresponding to theobservation pixel of the output image is set to be the center isillustrated. Furthermore, at this point, in a case of the pixels in theBayer layout that is configured from a unit of four pixels (one Rcomponent pixel, one B component pixel, and two G component pixels), anexample of the structure of the class tap or the prediction tap in whicheach of the four unit pixels is set to be the central pixel isillustrated.

FIG. 8A is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Rcomponent pixel is set to be the central pixel.

FIG. 8B is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Gcomponent pixel is set to be the central pixel.

FIG. 8C is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, thedifferent G component pixel is set to be the central pixel.

FIG. 8D is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Bcomponent pixel is set to be the central pixel.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures. Furthermore, in the classtap, the G class tap, the R class tap, and the B class tap may have thesame structures or may have different structures. In the same manner, inthe prediction tap, the G prediction tap, the R prediction tap, and theB prediction tap may have the same structures or may have differentstructures.

FIG. 9 is a flow chart describing an example of the image processing bythe image processing apparatus 100 in FIG. 2.

In Step S21, it is determined whether or not the image (the input image)intended for a target of image processing is input and the processingwaits until it is determined that the image intended for a target ofimage processing is input. In Step S21, the processing proceeds to StepS22 if it is determined that the image is input.

Moreover, as illustrated, the input image, for example, is set to be animage that is configured from the output values from the image sensor inwhich the color filter array in the Bayer layout is used. Therefore, inthe input image, the image signal of the R component is obtained fromthe pixel on which the R color filter is arranged, but the image signalsof the G component and the B component are not obtained. In the samemanner, only the image signal of the G component is obtained from a Gpixel, the image signals of the R component and the B component are notobtained. Then, only the image signal of the B component is obtainedfrom the B pixel and the image signals of the R component and the Gcomponent are not obtained.

In Step S22, the observation pixel is set. By doing this, the centralpixel is decided in the input image.

In Step S23, the representative RGB calculation unit 101 performsrepresentative RGB calculation processing that is described belowreferring to FIG. 10. By doing this, the representative value Dg, therepresentative value Dr, and the representative value Db are calculated.

In Step S24, the G class tap selection unit 102-1, the R class tapselection unit 102-2, and the B class tap selection unit 102-3 acquirethe G class tap, the R class tap, and the B class tap, respectively.

Moreover, if the G output image is generated, the G class tap isacquired, if the R output image is generated, the R class tap isacquired, and if the B output image is generated, the B class tap isacquired. From now on, for brief description, a case where the G outputimage is generated is described.

In Step S25, color conversion is performed. For example, in a case wherethe G output image is generated, the G conversion unit 105-11 performs Gconversion. At this time, the conversion value G′, the conversion valueR′, and the conversion value B′ are calculated using Equations (1) to(3) described above.

In Step S26, class classification is performed. For example, if the Goutput image is generated, the G class classification unit 106-1performs the class classification by coding the supplied G class tap byperforming the adaptive dynamic range coding (ADRC) and thus generatingthe class code.

In Step S27, the prediction tap is acquired. For example, if the Goutput image is generated, the G prediction tap selection unit 103-1acquires the G prediction tap.

In Step S28, the color conversion is performed. For example, if the Goutput image is generated, the G conversion unit 105-12 performs the Gconversion. At this time, the conversion value G′, the conversion valueR′, and the conversion value B′ are calculated using Equations (1) to(3) described above.

In Step S29, the coefficient is read. For example, if the G output imageis generated, the coefficient that is stored with it being mapped to theclass code generated in Step S26 is read from the G coefficient memory107-1.

In Step S30, an observation pixel value is predicted. For example, ifthe G output image is generated, the G prediction tap that iscolor-converted by the processing in Step S28 is substituted for thepixels x₁, x₂, and so forth up to x_(N) in Equation (4), the coefficientthat is read by the processing in Step S29 is supplied as the tapcoefficient w_(n) in Equation (4), and the G product and sum calculationunit 108-1 performs the calculation in Equation (4), thereby predictingthe pixel value of the observation image of the output image.

In Step S31, it is determined whether or not a following observationpixel is present. If it is determined that the next observation pixel ispresent, the processing returns to Step S22. Then, Step S22 and thesubsequent steps are repeatedly performed.

In Step S31, if it is determined that the next observation pixel is notpresent, the processing stops.

Thus, image generation processing is performed.

Next, a detailed example of the representative RGB calculationprocessing in Step S23 in FIG. 9 is described referring to the flowchart in FIG. 10.

In Step S41, the representative RGB calculation unit 101 calculates theinterpolation values g of the R component pixel and the B componentpixel in the designation area in the input image. At this time, forexample, as illustrated in FIG. 4, the input value G1 to the input valueG4 of the pixels G1 to G4, four G pixels in the vicinity (in the upward,downward, leftward, and rightward directions) of the central pixelwithin the designation area are averaged and thus the interpolationvalue g, a value of the interpolated G component in the pixel positionof the central pixel is calculated.

In Step S42, the representative RGB calculation unit 101 calculates therepresentative value Dg. At this time, an average of the input values Gof all the G pixels within the designation area and the interpolationvalue g calculated in Steps S41 is calculated as the representativevalue Dg.

In Step S43, the representative RGB calculation unit 101 calculates theinterpolation value r of a G component pixel. For example, if theinterpolation value r in the position indicated by the pixel G1 or thepixel G4 in FIG. 4 is calculated, as illustrated in FIG. 5, the averagevalue of a pixel R1 and a pixel R2 that are positioned immediately tothe left of the G pixel and immediately to the right of the G pixel,respectively, is set to be the interpolation value r.

By doing this, the input value G and the interpolation value r can beobtained in the pixel position of the G pixel within the designationarea, and the input value R and the interpolation value g can beobtained in the pixel position of the R pixel within the designationarea.

In Step S44, the representative RGB calculation unit 101 calculates therepresentative value Dr. At this time, in each pixel position, (theinterpolation value r−the input value G) and (the input value R−theinterpolation value g) are calculated, and the representative value Dris calculated as the value that results from adding the representativevalue Dg to the average value of the calculated (the interpolation valuer−the input value G) and the calculated (the input value R−theinterpolation value g).

In Step S45, the representative RGB calculation unit 101 calculates aninterpolation value b of the G component pixel. For example, if theinterpolation value b in the position indicated by the pixel G1 or thepixel G4 in FIG. 4 is calculated, as illustrated in FIG. 6, the averagevalue of a pixel B1 and a pixel B2 that are positioned immediately overthe G pixel and immediately under the G pixel, respectively, is set tobe the interpolation value b.

By doing this, the input value G and the interpolation value b can beobtained in the pixel position of the G pixel within the designationarea, and the input value B and the interpolation value g can beobtained in the pixel position of the B pixel within the designationarea.

In Step S46, the representative RGB calculation unit 101 calculates therepresentative value Db. At this time, in each pixel position, (theinterpolation value b−the input value G) and (the input value B−theinterpolation value g) are calculated, and the representative value Dbis calculated as the value that results from adding the representativevalue Dg to the average value of the calculated (the interpolation valueb−the input value G) and the calculated (the input value B−theinterpolation value g).

Thus, the representative RGB calculation processing is performed.

Next, an example of coefficient learning processing by the learningapparatus 200 in FIG. 7 is described referring to a flow chart in FIG.11.

In Step S61, it is determined whether or not the teacher image is input,and the processing waits until it is determined that the teacher imageis input. In Step S61, if it is determined that the teacher image isinput, the processing proceeds to Step S62.

Moreover, as described, for example, the teacher images are set to bethe G component image, the R component image, and the B component imagethat are obtained by arranging the three image sensors that correspondto the R component, the G component, and the B component, respectively,in the frame 14 in FIG. 1.

In Step S62, the student image generation unit 202 generates the studentimage. At this time, for example, by using the simulation model for theoptical low pass filter and so forth, the teacher image is degraded andthe image that is output from the image sensor that is configured fromthe pixels arranged according to the Bayer layout is generated and isset to be the student image.

In Step S63, the observation pixel selection unit 201 selects (sets) anarbitrary one pixel, as the observation pixel, from the teacher images.By doing this, the central pixel in the student image is decided.

In Step S64, the representative RGB calculation unit 203 performs therepresentative RGB calculation processing that is described referring tothe flow chart in FIG. 10. By doing this, the representative value Dg,the representative value Dr, the representative value Db are calculated.

In Step S65, the class tap selection unit 204 selects the class tap fromthe pixels within the designation area in the student image and acquiresthe class tap.

Moreover, if the observation pixel selection unit 201 selects theobservation pixel from the G component image among the teacher images,the class tap selection unit 204 is set in such a manner that it selectsthe G class tap. Furthermore, if the observation pixel selection unit201 selects the observation pixel from the R component image among theteacher images, the class tap selection unit 204 is set to select the Rclass tap, and if the observation pixel selection unit 201 selects theobservation pixel from the B component image among the teacher images,the class tap selection unit 204 is set to select the B class tap.

In Step S66, the color conversion unit 206-1 performs a predeterminedconversion processing on the class tap that is acquired by theprocessing in Step S65.

At this point, if the G class tap is acquired by the class tap selectionunit 204, the color conversion unit 206-1 is set to perform the Gconversion processing. Furthermore, if the R class tap is acquired bythe class tap selection unit 204, the color conversion unit 206-1 is setto perform the R conversion processing, and if the B class tap isacquired by the class tap selection unit 204, the color conversion unit206-1 is set to perform the B conversion processing.

In Step 67, the class classification unit 207 codes the supplied classtap by performing the adaptive dynamic range coding (ADRC), and thusgenerates a class code. The class code generated here is supplied to thenormal equation addition unit 208, along with the class tap.

In Step S68, the prediction tap selection unit 205 selects theprediction tap from the pixels within the designation area in thestudent image and acquires the prediction tap.

At this point, if the observation pixel selection unit 201 selects theobservation pixel from the G component image among the teacher images,the prediction tap selection unit 205 is set to select the G predictiontap. Furthermore, if the observation pixel selection unit 201 selectsthe observation pixel from the R component image among the teacherimages, the prediction tap selection unit 205 is set to select the Rprediction tap, and if the observation pixel selection unit 201 selectsthe observation pixel from the B component image among the teacherimages, the prediction tap selection unit 205 is set to select the Bprediction tap.

In Step S69, the color conversion unit 206-2 performs a predeterminedconversion processing on the prediction tap that is acquired in StepS68.

At this point, if the G prediction tap is acquired by the prediction tapselection unit 205, the color conversion unit 206-2 is set to performthe G conversion processing. Furthermore, if the R prediction tap isacquired by the prediction tap selection unit 205, the color conversionunit 206-2 is set to perform the R conversion processing, and if the Bprediction tap is acquired by the prediction tap selection unit 205, thecolor conversion unit 206-2 is set to perform the B conversionprocessing.

In Step S70, the normal equation addition unit 208 performs adding ofthe normal equation.

As described above, the normal equation addition unit 208 generates, forexample, the linear simple equation expressed in Equation (4) describedabove and the class tap that goes through the processing by the colorconversion unit is used as the pixels x₁, x₂, and so forth up to x_(N)in Equation (4). Then, the normal equation addition unit 208 adds thelinear simple equation generated in this manner to every class codegenerated in Step S67 and thus generates the normal equation in Equation(11).

In Step S71, it is determined whether or not the following observationpixel is present. If it is determined that the next observation pixel ispresent, the processing returns to Step S63. Then, Step S63 and thesubsequent steps are repeatedly performed.

On the one hand, in Step S71, if it is determined that the nextobservation pixel is not present, the processing proceeds to Step S72.

In Step S72, the coefficient data generation unit 209 calculates thecoefficient.

At this time, as described above, the coefficient data generation unit209 solves the normal equation in Equation (11) for the tap coefficientw_(n) by using the sweep-out method (the Gauss-Jordan eliminationmethod) and the like. Then, according to a type of teacher image (the Gcomponent image, the R component image, or the B component image) inwhich the observation pixel is set, the coefficient data generation unit209 outputs the obtained tap coefficient w_(n), as a G coefficientnecessary for performing the prediction calculation of the G outputimage, an R coefficient necessary for performing the predictioncalculation of the R output image, or a B coefficient necessary forperforming the prediction calculation of the B output image.

Thus, the G coefficient, the R coefficient, the B coefficient for everyclass code obtained are stored in the G coefficient memory 107-1, the Rcoefficient memory 107-2, and the B coefficient memory 107-3 in FIG. 2,respectively, and are set to be read in the processing in Step S29 inFIG. 9.

Thus, the coefficient learning processing is performed.

Incidentally, according to the embodiment described referring to FIG. 2,the G output image, the R output image, and the B output image are setto be obtained at the same time. However, because the number of G pixelsis greater per unit of area in the Bayer layout, prediction accuracy ishigh. Furthermore, G is higher in S/N ratio than R or B because ofcharacteristics of a color filter. Because of this, for example, the Goutput image may be generated first, and then the R output image and theB output image may be set to be generated using the generated G outputimage. By doing this, the image processing that provides higher qualityin terms of an amount of noise and resolution (frequencycharacteristics) can be performed.

FIG. 12 is a block diagram illustrating a configuration example of animage processing apparatus according to another embodiment, to which thepresent technology is applied. An image processing apparatus 150illustrated in FIG. 12 is first set to generate the G output image andthen is set to generate the R output image and the B output image usingthe generated G output image.

A representative RGB calculation unit 151 in FIG. 12 has the sameconfiguration as the representative RGB calculation unit 101 in FIG. 2,and thus a detailed description thereof is omitted.

Furthermore, a G class tap selection unit 152-1, a G conversion unit155-11, a G class classification unit 156-1, a G coefficient memory157-1, a G prediction tap selection unit 153-1, a G conversion unit155-12, and a G product and sum calculation unit 158-1, each of which isa functional block relating to the generation of the G output image inFIG. 12, have the same configurations as the G class tap selection unit102-1, the G conversion unit 105-11, the G class classification unit106-1, the G coefficient memory 107-1, the G prediction tap selectionunit 103-1, the G conversion unit 105-12, and the G product and sumcalculation unit 108-1 that are illustrated in FIG. 2, respectively, andthus detailed descriptions thereof are omitted.

In a case of the configuration in FIG. 12, unlike in a case of theconfiguration in FIG. 2, the data that is output from the G product andsum calculation unit 108-1 is set in such a manner that the data issupplied through a delay unit 161-1 to an R class tap selection unit152-2 and an R prediction tap selection unit 153-2, and to a B class tapselection unit 152-3 and a B prediction tap selection unit 153-3.Furthermore, in a case of the configuration in FIG. 12, unlike in a caseof the configuration in FIG. 2, the data that is output from therepresentative RGB calculation unit 151 is set in such a manner that thedata is supplied through a delay unit 161-2 to an R conversion unit155-21 and an R conversion unit 155-22, and to a B conversion unit155-31 and a B conversion unit 155-32.

In a case of the configuration in FIG. 12, the R class tap selectionunit 152-2 selects from the G output image an R class tap, a class tapnecessary for generating the R component image and thus acquires the Rclass tap. The R class tap, for example, is configured from apredetermined number of pixels in which the pixel of a G output image inthe position corresponding to the observation pixel of the output imageis set to be the central pixel and the central pixel is set to be thecenter.

The R class tap selected by the R class tap selection unit 152-2 issupplied to the R conversion unit 155-21. The R conversion unit 155-21performs R conversion processing on each pixel value that makes up the Rclass tap.

The R conversion processing here, for example, is performed as follows.

In FIG. 12, unlike in FIG. 2, the R class tap is selected from the Goutput image by the R class tap selection unit 152-2. Therefore, in thiscase, the R class tap is configured from all the G component pixels.Now, the G component pixel of the G output image is indicated by aprediction value Gp.

The R conversion unit 155-21 calculates a conversion value Gp′ byperforming the calculation in Equation (18) on each pixel value thatmakes up the R class tap.

Gp′=Gp−(Dg−Dr)  (18)

The correlation between the pixel values, each of which makes up the Rclass tap, is increased by performing the R conversion processing. Thatis, the pixel value of the G output image is offset with the pixel valueof the R pixel of the input image being set to be the reference, andthus the change due to the difference in color component between thepixel values, each of which makes up the R class tap, can be removed.

The R class tap that is output from the R conversion unit 155-21 issupplied to an R class classification unit 156-2. Moreover, the R classtap that is output from the R conversion unit 155-21 is configured fromthe conversion value Gp′ that is calculated using Equation (18)described above.

The R class classification unit 156-2 codes the supplied R class tap byperforming the adaptive dynamic range coding (ARRC), and thus generatesa class code. The class code generated here is output to the Rcoefficient memory 157-2.

The R coefficient memory 157-2 reads the coefficient that is stored withit being mapped to the class code that is output from the R classclassification unit 156-2. Then, the R coefficient memory 157-2 suppliesthe read coefficient to an R product and sum calculation unit 158-2.Moreover, the coefficient that is a coefficient obtained by priorlearning and that is used in the product and sum calculation describedbelow is stored in the R coefficient memory 157-2 with it being mappedto the class code.

Moreover, if the image processing apparatus 150 as configured in FIG. 12is used, even when the learning is performed on the coefficient that isstored in an R coefficient memory 157-2, the learning for generating theR output image with the G output image being set to be the teacher imageis set to be performed as well.

The R prediction tap selection unit 153-2 selects from the G outputimage an R prediction tap that is a prediction tap necessary forgenerating the R component image and acquires the R prediction tap. TheR prediction tap, for example, is configured from a predetermined numberof pixels in which the pixel of the G output image in the positioncorresponding to the observation pixel of the output image is set to bethe central pixel and the central pixel is set to be the center.Moreover, in FIG. 12, unlike in FIG. 2, the R prediction tap is selectedfrom the G output image by the R prediction tap selection unit 153-2.Therefore, in this case, the R prediction tap is configured from all theG component pixels.

The R prediction tap selected by the R prediction tap selection unit153-2 is supplied to the R conversion unit 155-22. The R conversion unit155-22 performs the R conversion processing on each pixel value thatmakes up the R prediction tap.

The R conversion processing by the R conversion unit 155-22 is the sameas that by the R conversion unit 155-21. That is, the conversion valueGp′ is calculated using Equation (18) described above.

The R prediction tap that is output from the R conversion unit 155-22 issupplied to the R product and sum calculation unit 158-2. Moreover, theR prediction tap that is output from the R conversion unit 155-21 isconfigured from the conversion value Gp′ that is calculated usingEquation (18) described above.

The R product and sum calculation unit 158-2 has the same configurationas the R product and sum calculation unit 108-2 in FIG. 2, but performsthe prediction calculation on the pixel value of the observation pixelin the R component image (referred to as the R output image) thatbecomes the output image, based on the R prediction tap.

In this manner, the R output image can be obtained by predicting eachobservation pixel.

Furthermore, in a case of the configuration in FIG. 12, the B class tapselection unit 152-3 selects from the G output image a B class tap, aclass tap necessary for generating the B component image and thusacquires the B class tap. The B class tap, for example, is configuredfrom a predetermined number of pixels in which the pixel of a G outputimage in the position corresponding to the observation pixel of theoutput image is set to be the central pixel and the central pixel is setto be the center.

The B class tap selected by the B class tap selection unit 152-3 issupplied to the B conversion unit 155-31. The B conversion unit 155-31performs the B conversion processing on each pixel value that makes upthe B class tap.

The B conversion processing here, for example, is performed as follows.

In FIG. 12, unlike in FIG. 2, the B class tap is selected from the Goutput image by the B class tap selection unit 152-3. Therefore, in thiscase, the B class tap is configured from all the G component pixels.Now, the G component pixel of the G output image is indicated by theprediction value Gp.

The B conversion unit 155-31 calculates the conversion value Gp′ byperforming the calculation in Equation (19) on each pixel value thatmakes up the B class tap.

Gp′=Gp−(Dg−Db)  (19)

The correlation between the pixel values, each of which makes up the Bclass tap, is increased by performing the B conversion processing. Thatis, the pixel value of the G output image is offset with the pixel valueof the B pixel of the input image being set to be the reference, andthus the change due to the difference in color component between thepixel values, each of which makes up the B class tap, can be removed.

The B class tap that is output from the B conversion unit 155-31 issupplied to a B class classification unit 156-3. Moreover, the B classtap that is output from the B conversion unit 155-31 is configured fromthe conversion value Gp′ that is calculated using Equation (19)described above.

The B class classification unit 156-3 codes the supplied B class tap byperforming the adaptive dynamic range coding (ADRC), and thus generatesa class code. The class code generated here is output to the Bcoefficient memory 157-3.

The B coefficient memory 157-3 reads the coefficient that is stored withit being mapped to the class code that is output from the B classclassification unit 156-3. Then, the B coefficient memory 157-3 suppliesthe read coefficient to a B product and sum calculation unit 158-3.Moreover, the coefficient that is a coefficient obtained by priorlearning and that is used in the product and sum calculation describedbelow is stored in the B coefficient memory 157-3 with it being mappedto the class code.

Moreover, if the image processing apparatus 150 as configured in FIG. 12is used, even when the learning is performed on the coefficient that isstored in the B coefficient memory 157-3, the learning for generatingthe B output image with the G output image being set to be the teacherimage is set to be performed as well.

The B prediction tap selection unit 153-3 selects from the G outputimage a B prediction tap that is a prediction tap necessary forgenerating the B component image and acquires the B prediction tap. TheB prediction tap, for example, is configured from a predetermined numberof pixels in which the pixel of the G output image in the positioncorresponding to the observation pixel of the output image is set to bethe central pixel and the central pixel is set to be the center.Moreover, in FIG. 12, unlike in FIG. 2, the B prediction tap is selectedfrom the G output image by the B prediction tap selection unit 153-3.Therefore, in this case, the B prediction tap is configured from all theG component pixels.

The B prediction tap selected by the B prediction tap selection unit153-3 is supplied to the B conversion unit 155-32. The B conversion unit155-32 performs the B conversion processing on each pixel value thatmakes up the B prediction tap.

The B conversion processing by the B conversion unit 155-32 is the sameas that by the B conversion unit 155-31. That is, the conversion valueGp′ is calculated using Equation (19) described above.

The B prediction tap that is output from the B conversion unit 155-32 issupplied to the B product and sum calculation unit 158-3. Moreover, theB prediction tap that is output from the B conversion unit 155-31 isconfigured from the conversion value Gp′ that is calculated usingEquation (19) described above.

The B product and sum calculation unit 158-3 has the same configurationas the B product and sum calculation unit 108-3 in FIG. 2, but performsthe prediction calculation on the pixel value of the observation pixelin the B component image (referred to as the B output image) thatbecomes the output image, based on the B prediction tap.

In this manner, the B output image can be obtained by predicting eachobservation pixel.

FIGS. 13A to 13D are diagrams, each illustrating an example of astructure of the G class tap or the G prediction tap that is acquired inthe image processing apparatus 150 in FIG. 12.

In each of the examples in FIGS. 13A to 13D, the class tap or theprediction tap that is configured from nine (=3×3) pixels in which thepixel (the central pixel) of the input image corresponding to theobservation pixel of the output image is set to be the center isillustrated. Furthermore, at this point, in a case of the pixels in theBayer layout that is configured from a unit of four pixels (one Rcomponent pixel, one B component pixel, and two G component pixels), anexample of the structure of the class tap or the prediction tap in whicheach of the four unit pixels is set to be the central pixel isillustrated.

FIG. 13A is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Rcomponent pixel is set to be the central pixel.

FIG. 13B is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Gcomponent pixel is set to be the central pixel.

FIG. 13C is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, thedifferent G component pixel is set to be the central pixel.

FIG. 13D is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Bcomponent pixel is set to be the central pixel.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures.

FIGS. 14A to 14D are diagrams, each illustrating an example of astructure of the R class tap or the R prediction tap that is acquired inthe image processing apparatus 150 in FIG. 12. As illustrated in FIGS.14A to 14D, because the R class tap or the R prediction tap is acquiredfrom the G output image, all the R class taps or all the R predictiontaps are all indicated by Gp within circles in the drawings.

In each of the examples in FIGS. 14A to 14D, the class tap or theprediction tap that is configured from the five pixels in the form of across in which the pixel (the central pixel) of the input imagecorresponding to the observation pixel of the output image is set to bethe center is illustrated. Furthermore, at this point, in a case of thepixels in the Bayer layout that is configured from a unit of four pixels(one R component pixel, one B component pixel, and two G componentpixels), an example of the structure of the class tap or the predictiontap in which each of the four unit pixels is set to be the central pixelis illustrated.

FIG. 14A is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Rcomponent pixel is set to be the central pixel.

FIG. 14B is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Gcomponent pixel is set to be the central pixel.

FIG. 14C is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, thedifferent G component pixel is set to be the central pixel.

FIG. 14D is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Bcomponent pixel is set to be the central pixel.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures.

FIGS. 15A to 15D are diagrams, each illustrating an example of astructure of the B class tap or the B prediction tap that is acquired inthe image processing apparatus 150 in FIG. 12. As illustrated in FIGS.15A to 15D, because the B class tap or the B prediction tap is acquiredfrom the G output image, all the B class taps or all the B predictiontaps are all indicated by Gp within circles in the drawings.

In each of the examples in FIGS. 15A to 15D, the class tap or theprediction tap that is configured from the five pixels in the form of across in which the pixel (the central pixel) of the input imagecorresponding to the observation pixel of the output image is set to bethe center is illustrated. Furthermore, at this point, in a case of thepixels in the Bayer layout that is configured from a unit of four pixels(one R component pixel, one B component pixel, and two G componentpixels), an example of the structure of the class tap or the predictiontap in which each of the four unit pixels is set to be the central pixelis illustrated.

FIG. 15A is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Rcomponent pixel is set to be the central pixel.

FIG. 15B is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Gcomponent pixel is set to be the central pixel.

FIG. 15C is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, thedifferent G component pixel is set to be the central pixel.

FIG. 15D is a diagram illustrating an example of the class tap or theprediction tap in the case where in the pixels in the Bayer layer, the Bcomponent pixel is set to be the central pixel.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures.

For example, the image processing that provides higher quality in termsof an amount of noise and resolution (frequency characteristics)compared to the configuration in FIG. 2 can be performed by configuringthe image processing apparatus as illustrated in FIG. 12.

In the examples that are described referring to FIG. 2 and FIG. 12, aconversion value is substituted for the pixel value by performing thecolor conversion, and thus the class classification, and the product andsum calculation are performed, but for example, a color difference maybe substituted for the pixel value, and thus the class classification,and the product and sum calculation may be performed.

FIG. 16 is a block diagram illustrating a configuration example of animage processing apparatus according to another embodiment, to which thepresent technology is applied. An image processing apparatus 180illustrated in FIG. 16 is first set to generate the G output image andthen is set to generate the R output image and the B output image usingthe generated G output image. Furthermore, when the R output image andthe B output image are generated using the generated G output image, thecolor difference is substituted for the pixel value, and thus the classclassification and the product and sum calculation are set to beperformed.

Because a representative RGB calculation unit 181 in FIG. 16 has thesame configuration as the representative RGB calculation unit 101 inFIG. 2, a detailed description thereof is omitted.

Furthermore, a G class tap selection unit 182-1, a G conversion unit185-11, a G class classification unit 186-1, a G coefficient memory187-1, a G prediction tap selection unit 183-1, a G conversion unit185-12, and a G product and sum calculation unit 188-1, each of which isa functional block relating to the generation of the G output image inFIG. 16, have the same configurations as the G class tap selection unit102-1, the G conversion unit 105-11, the G class classification unit106-1, the G coefficient memory 107-1, the G prediction tap selectionunit 103-1, the G conversion unit 105-12, and the G product and sumcalculation unit 108-1 that are illustrated in FIG. 2, respectively, andthus detailed descriptions thereof are omitted.

In a case of the configuration in FIG. 16, unlike in a case of theconfiguration in FIG. 2, the input image is set in such a manner thatthe input image is supplied through a delay unit 191-1 to an R class tapselection unit 182-2 and an R prediction tap selection unit 183-2, andto a B class tap selection unit 182-3 and the B prediction tap selectionunit 183-3.

Furthermore, in the case of the configuration in FIG. 16, unlike in acase of the configuration in FIG. 2, the data that is output from the Gproduct and sum calculation unit 108-1 is set in such a manner that thedata is supplied to an R conversion unit 189-2 and a B conversion unit189-3.

Furthermore, in the case of the configuration in FIG. 16, the data thatis output from the representative RGB calculation unit 181 is set insuch a manner that the data is supplied through a relay unit 191-2 to an(R−G) conversion unit 185-21 and an (R−G) conversion unit 185-22, and toa (B−G) conversion unit 185-31 and an (B−G) conversion unit 185-32.

Additionally, in a case where the configuration in FIG. 16 is employed,the R class tap, the B class tap, the R prediction tap, and the Bprediction tap are different in structure from those in FIG. 2 or inFIG. 12, respectively. Moreover, even though the configuration in FIG.16 is employed, the G class tap and the G prediction tap are the same instructure as those described above referring to FIG. 13, respectively.

FIGS. 17A to 17D are diagrams, each illustrating an example of astructure of the R class tap or the R prediction tap that is acquired inthe image processing apparatus 180 in FIG. 16.

In each of the examples in FIGS. 17A to 17D, the class tap or theprediction tap is illustrated that is configured from the five pixels inthe form of a cross. Furthermore, at this point, in a case of the pixelsin the Bayer layout that is configured from a unit of four pixels (one Rcomponent pixel, one B component pixel, and two G component pixels), anexample of the structure of the class tap or the prediction tap in whicheach of the four unit pixels is set to be the central pixel isillustrated.

FIG. 17A is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Rcomponent pixel is set to be the central pixel.

FIG. 17B is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Gcomponent pixel is set to be the central pixel.

FIG. 17C is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, thedifferent G component pixel is set to be the central pixel.

FIG. 17D is a diagram illustrating an example of the class tap or theprediction tap in the case where in the pixels in the Bayer layer, the Bcomponent pixel is set to be the central pixel.

As illustrated in FIGS. 17A to 17D, the five pixels that make up the Rclass tap or the R prediction tap are all set to be the R componentpixels, and do not include the G component pixel and the B componentpixel. Furthermore, in FIGS. 17B to 17D, the pixel that is the center ofthe R class tap or the R prediction tap is set to be the pixel in aposition adjacent to the original center pixel that is indicated by ahatched circle in the drawings.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures.

FIGS. 18A to 18D are diagrams, each illustrating an example of astructure of the B class tap or the B prediction tap that is acquired inthe image processing apparatus 180 in FIG. 16.

In each of the examples in FIGS. 18A to 18D, the class tap or theprediction tap is configured from the five pixels in the form of across. Furthermore, at this point, in a case of the pixels in the Bayerlayout that is configured from a unit of four pixels (one R componentpixel, one B component pixel, and two G component pixels), an example ofthe structure of the class tap or the prediction tap in which each ofthe four unit pixels is set to be the central pixel is illustrated.

FIG. 18A is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Rcomponent pixel is set to be the central pixel.

FIG. 18B is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Gcomponent pixel is set to be the central pixel.

FIG. 18C is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, thedifferent G component pixel is set to be the central pixel.

FIG. 18D is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Bcomponent pixel is set to be the central pixel.

As illustrated in FIGS. 18A to 18D, the five pixels that make up the Bclass tap or the B prediction tap are all set to be the B componentpixel, and do not include the G component pixel and the R componentpixel. Furthermore, in FIGS. 18A to 18C, the pixel that is the center ofthe B class tap or the B prediction tap is set to be the pixel in aposition adjacent to the original center pixel that is indicated by ahatched circle in the drawings.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures.

Referring back to FIG. 16, the R class tap selection unit 182-2 selectsfrom the input image the R class tap that is a class tap necessary forgenerating the R component image and acquires the R class tap.

The R class tap selected by the R class tap selection unit 152-2 issupplied to the (R−G) conversion unit 185-21. The (R−G) conversion unit185-21 is set to perform (R−G) conversion processing on each pixel valuethat makes up the R class tap, and a virtual color difference iscalculated by the (R−G) conversion processing.

That is, the (R−G) conversion unit 185-21 performs the calculation inEquation (20) on each pixel value that makes up the R class tap and thuscalculates a virtual color difference RGc.

RGc=R−g  (20)

Moreover, the interpolation value g in Equation (20) is supplied fromthe representative RGB calculation unit 181.

The R class tap that is output from the (R−G) conversion unit 185-21 issupplied to an (R−G) class classification unit 186-2. Moreover, the Rclass tap that is output from the (R−G) conversion unit 185-21 isconfigured from the virtual color difference RGc that is calculatedusing Equation (20) described above.

The (R−G) class classification unit 186-2 codes the supplied R class tapby performing the adaptive dynamic range coding (ARRC), and thusgenerates a class code. The class code generated here is output to an(R−G) coefficient memory 187-2.

The (R−G) coefficient memory 187-2 reads the coefficient that is storedwith it being mapped to the class code that is output from the (R−G)class classification unit 186-2. Then, the (R−G) coefficient memory187-2 supplies the read coefficient to an (R−G) product and sumcalculation unit 188-2. Moreover, the coefficient that is a coefficientobtained by prior learning and that is used in the product and sumcalculation described below is stored in the (R−G) coefficient memory187-2 with it being mapped to the class code.

Moreover, if the image processing apparatus 180 as configured in FIG. 16is used, even when the learning is performed on the coefficient that isstored in the (R−G) coefficient memory 187-2, the learning forgenerating the R output image with the virtual color difference beingset to be the class tap or the prediction tap is set to be performed aswell.

The R prediction tap selection unit 183-2 selects from the input imagean R prediction tap that is a prediction tap necessary for generatingthe R component image and acquires the R prediction tap.

The R prediction tap selected by the R prediction tap selection unit183-2 is supplied to the (R−G) conversion unit 185-22. The (R−G)conversion unit 185-22 is set to perform (R−G) conversion processing oneach pixel value that makes up the R prediction tap, and the virtualcolor difference is calculated by the (R−G) conversion processing.

The (R−G) conversion processing by the (R−G) conversion unit 185-22 isthe same as that by the (R−G) conversion unit 185-21. That is, thevirtual color difference RGc is calculated using Equation (20) describedabove.

The R prediction tap that is output from the (R−G) conversion unit185-22 is supplied to the (R−G) product and sum calculation unit 188-2.Moreover, the R prediction tap that is output from the (R−G) conversionunit 185-21 is configured from the virtual color difference RGc that iscalculated using Equation (20) described above.

The (R−G) product and sum calculation unit 188-2 performs the predictioncalculation on (R−G) color difference of the observation pixel in the Rcomponent image (referred to as the R output image) that becomes theoutput image, based on the R prediction tap.

The R conversion unit 189-2 converts a prediction value (R−G)p of the(R−G) difference of the observation pixel that is output from the (R−G)product and sum calculation unit 188-2, for example, into a predictionvalue Rp for the pixel value of the R component by the calculation usingEquation (21).

Rp=(R−G)p+Gp  (21)

In this manner, the R output image can be obtained by predicting eachobservation pixel.

The B class tap selection unit 182-3 selects from the input image a Bclass tap that is a class tap necessary for generating the B componentimage and acquires the B class tap.

The B class tap selected by the B class tap selection unit 152-3 issupplied to the (B−G) conversion unit 185-31. The (B−G) conversion unit185-31 is set to perform (B−G) conversion processing on each pixel valuethat makes up the B class tap, and the virtual color difference iscalculated by the (B−G) conversion processing.

That is, the (B−G) conversion unit 185-31 performs the calculation inEquation (22) on each pixel value that makes up the B class tap and thuscalculates a virtual color difference BGc.

BGc=B−g  (22)

Moreover, the interpolation value g in Equation (22) is supplied fromthe representative RGB calculation unit 181.

The B class tap that is output from the (B−G) conversion unit 185-31 issupplied to a B class classification unit 186-3. Moreover, the B classtap that is output from the (B−G) conversion unit 185-31 is configuredfrom the virtual color difference BGc that is calculated using Equation(20) described above.

The B class classification unit 186-3 codes the supplied B class tap byperforming the adaptive dynamic range coding (ARRC), and thus generatesa class code. The class code generated here is output to a (B−G)coefficient memory 187-3.

The (B−G) coefficient memory 187-3 reads the coefficient that is storedwith it being mapped to the class code that is output from the B classclassification unit 186-3. Then, the (B−G) coefficient memory 187-3supplies the read coefficient to a (B−G) product and sum calculationunit 188-3. Moreover, the coefficient that is a coefficient obtained byprior learning and that is used in the product and sum calculationdescribed below is stored in the (B−G) coefficient memory 187-3 with itbeing mapped to the class code.

Moreover, if the image processing apparatus 180 as configured in FIG. 16is used, even when the learning is performed on the coefficient that isstored in the (B−G) coefficient memory 187-3, the learning forgenerating the B output image with the virtual color difference beingset to be the class tap or the prediction tap is set to be performed aswell.

The B prediction tap selection unit 183-3 selects from the input image aB prediction tap that is a prediction tap necessary for generating the Bcomponent image and acquires the B prediction tap.

The B prediction tap selected by the B prediction tap selection unit183-3 is supplied to the (B−G) conversion unit 185-32. The (B−G)conversion unit 185-32 is set to perform (B−G) conversion processing oneach pixel value that makes up the B prediction tap, and the virtualcolor difference is calculated by the (B−G) conversion processing.

The (B−G) conversion processing by the (B−G) conversion unit 185-32 isthe same as that by the (B−G) conversion unit 185-31. That is, thevirtual color difference BGc is calculated using Equation (22) describedabove.

The B prediction tap that is output from the (B−G) conversion unit185-32 is supplied to the (B−G) product and sum calculation unit 188-3.Moreover, the B prediction tap that is output from the (B−G) conversionunit 185-31 is configured from the virtual color difference BGc that iscalculated using Equation (22) described above.

The (B−G) product and sum calculation unit 188-3 performs the predictioncalculation on (B−G) color difference of the observation pixel in the Bcomponent image (referred to as the B output image) that becomes theoutput image, based on the B prediction tap.

The B conversion unit 189-3 converts a prediction value (B−G)p of the(B−G) color difference of the observation pixel that is output from the(B−G) product and sum calculation unit 188-3, for example, into aprediction value Bp for the pixel value of the B component by thecalculation using Equation (23).

Bp=(B−G)p+Gp  (23)

In this manner, the B output image can be obtained by predicting eachobservation pixel.

Furthermore, when the virtual color difference is calculated, the pixelvalue of each color component, for example, may be multiplied by acoefficient that is a matrix coefficient that is stipulated in BT 709,BT 601 and the like and that is used in performing a conversion from RGBinto pb or pr. By doing this, a higher S/N ratio can be realized in theoutput image.

The example is described above in which each of the pixel values of theG output images with the same resolution, the R output image and the Boutput image is generated (predicted) based on the pixel value of theimage corresponding to the image signal that is output from the imagesensor of the one-chip camera.

However, each of the pixel values of the G output images with thedifferent resolutions, the R output image, and the B output image alsocan be generated (predicted) based on the pixel value of the imagecorresponding to the image signal that is output from the image sensorof the one-chip camera. For example, each of the pixel values of the Goutput image, the R output image, and the B output image, each of whichhas the number of the pixels that exceeds the number of pixels that arearranged in the image sensor of the one-chip camera can be generated(predicted) and the resolution can be converted.

FIGS. 19A to 19D are diagrams for describing a relationship between theclass tap or the prediction tap in a case of converting the resolutionof the image configured from the pixels in the Bayer layout andpositions of the pixels of the G output image, the R output image, andthe B output image in the image processing apparatus 100 that isdescribed above referring to FIG. 2. In each of the examples in FIGS.19A to 19D, the class tap or the prediction tap is configured from nine(=3×3) pixels in which the central pixel is set to be the center.

In FIG. 19A, among the pixels in the Bayer layout in the image sensor ofthe one-chip camera, the relationship is illustrated between the classtap or the prediction tap in a case where the R component pixel is setto be the central pixel and the positions of the pixels of the G outputimage, the R output image, and the B output image. In FIG. 19A, thepixel that is indicated by R written within a hatched circle is set tobe the central pixel, and small black circles are set to be thepositions of the pixels of the G output image, the R output image, andthe B output image.

As illustrated in FIG. 19A, the pixels of the G output image, the Routput image, and the B output image are arranged in the four positionsin the vicinity of the R component pixel that is the central pixel. Thatis, four types of pixel values are predicted (generated) based on thenine prediction taps illustrated in FIG. 19A. Moreover, at this point,the G prediction tap, the R prediction tap, and the B prediction tap areset to have the same structure, and the four types of pixel values areset to be predicted (generated) for each of the G output image, the Routput image, and the B output image.

In FIG. 19B, among the pixels in the Bayer layout in the image sensor ofthe one-chip camera, the relationship is illustrated between the classtap or the prediction tap in a case where the G component pixel is setto be the central pixel and the positions of the pixels of the G outputimage, the R output image, and the B output image. In FIG. 19B, thepixel that is indicated by G written within a hatched circle is set tobe the central pixel, and small black circles are set to be thepositions of the pixels of the G output image, the R output image, andthe B output image.

As illustrated in FIG. 19B, the pixels of the G output image, the Routput image, and the B output image are arranged in the four positionsin the vicinity of the G component pixel that is the central pixel. Thatis, the four types of pixel values are predicted (generated) based onthe nine prediction taps illustrated in FIG. 19B. Moreover, at thispoint, the G prediction tap, the R prediction tap, and the B predictiontap are set to have the same structure, and the four types of pixelvalues are predicted (generated) for each of the G output image, the Routput image, and the B output image.

In the same manner, in FIG. 19C, among the pixels in the Bayer layout inthe image sensor of the one-chip camera, the relationship is illustratedbetween the class tap or the prediction tap in a case where thedifferent G component pixel is set to be the central pixel and thepositions of the pixels of the G output image, the R output image, andthe B output image. Furthermore, in FIG. 19D, among the pixels in theBayer layout in the image sensor of the one-chip camera, therelationship is illustrated between the class tap or the prediction tapin a case where the B component pixel is set to be the central pixel andthe positions of the pixels of the G output image, the R output image,and the B output image.

Incidentally, in recent years, a contrivance has been considered thatincreases pixel density of the image sensor of the one-chip camera. Forexample, the pixel density can be increased by obliquely changing thearrangement of the pixels in the Bayer layout in the image sensor.

For example, the arrangement of the pixels in the Bayer layout asillustrated in FIG. 20 is changed as illustrated in FIG. 21. The pixelsof the image sensor are indicated by a small rectangle in FIG. 20 andFIG. 21, and letters R, G, and B written within the rectangles indicatethe color components of each pixel.

When the pixels of the image sensor are arranged, for example, there isa limit to distance between the adjacent pixels in the upward, downward,leftward, and rightward directions in order to avoid a mixture of lightand the like, and thus it is not possible to reduce the distance betweenthe adjacent pixels to smaller than a given distance. However, theoblique arrangement of the pixels can increase the number of pixels perunit of area while maintaining the distance between the adjacent pixelsin the upward, downward, leftward, and rightward directions.

For example, if the arrangement of the pixels in the Bayer layoutillustrated in FIG. 20 is changed as illustrated in FIG. 21, twice thenumber of pixels can be arranged per the same area.

At this point, the arrangement of the pixels as illustrated in FIG. 21is referred to as an oblique Bayer arrangement.

FIGS. 22A and 22B are diagrams for describing a relationship between theclass tap or the prediction tap in a case of converting the resolutionof the image configured from the pixels in the oblique Bayer layout andthe positions of the pixels of the G output image, the R output image,and the B output image in the image processing apparatus 100 that isdescribed above referring to FIG. 2. In examples in FIGS. 22A and 22B,the class tap or the prediction tap is configured from nine (=3×3)pixels in which the central pixel is set to be the center. In FIG. 19B,the pixel that is indicated by G written within a hatched circle is setto be the central pixel, and small black circles are set to be thepositions of the pixels of the G output image, the R output image, andthe B output image.

As illustrated in FIGS. 22A and 22B, the pixels of the G output image,the R output image, and the B output image are arranged between the Gcomponent pixel and the R component pixel or between the G componentpixel and the B component pixel in the input image. Therefore, the classtap or the prediction tap in a case of converting the resolution of theimage that is configured from the pixels in the oblique Bayer layout issuch that the G component pixel is set to be the center, and the classtap or the prediction tap is acquired as illustrated in FIG. 22A or FIG.22B.

Moreover, at this point, the G prediction tap, the R prediction tap, andthe B prediction tap are set to have the same structure, and asillustrated in FIG. 22A or FIG. 22B, the pixel values are set to bepredicted (generated) for each of the G output image, the R outputimage, and the B output image.

Furthermore, even though the resolution of the image is converted, forexample, the G output image may be generated first, and then the Routput image and the B output image may be set to be generated using thegenerated G output image. By doing this, the image processing thatprovides higher quality in terms of an amount of noise and resolution(frequency characteristics) can be performed.

FIGS. 23A to 23D are diagrams, each illustrating an example of the Rclass tap or the R prediction tap, or an example of the B class tap orthe B prediction tap in a case of converting the resolution of the imageconfigured from the pixels in the Bayer layout, in the image processingapparatus 150 that is described above referring to FIG. 12. In theexamples in FIGS. 23A to 23D, the G class tap or the G prediction tap isconfigured from nine (=3×3) pixels in which the central pixel is set tobe the center. As described above, the image processing apparatus 150 isfirst set to generate the G output image and then is set to generate theR output image and the B output image using the generated G outputimage.

Moreover, in the image processing apparatus 150 that is described abovereferring to FIG. 12, the relationship between the class tap or theprediction tap in the case of converting the resolution of the imageconfigured from the pixels in the Bayer layout and the position of thepixel of the G output image is the same as in FIGS. 19A to 19D.

Therefore, in the image processing apparatus 150 that is described abovereferring to FIG. 12, the G prediction tap (or the G class tap) and theR prediction tap (or the R class tap) are different in structure fromeach other in the case of converting the resolution of the imageconfigured from the pixels in the Bayer layout. Furthermore, the Gprediction tap (or the G class tap) and the B prediction tap (or the Bclass tap) are different in structure from each other.

In FIGS. 23A to 23D, the pixel of the input image is indicated by acircle within which a letter R, G, or B is written, and four whitecircles in the vicinity of the circle within which the letter R, G, or Bis written indicate the pixels of the G output image. Then, in the Goutput image, what are indicated by dotted line circles are set to bethe class tap or the prediction tap, and a black dotted line circleindicates the central pixel of the class tap or the prediction tap.

In FIG. 23A, in the case where the G prediction tap (or the G class tap)at the time of the generation of the G output image is such that the Rcomponent pixel is set to be the central pixel among the pixels in theBayer layout in the image sensor of the one-chip camera, the Rprediction tap (or the R class tap) or the B prediction tap (or the Bclass tap) is illustrated.

In FIG. 23B, in the case where the G prediction tap (or the G class tap)at the time of the generation of the G output image is such that the Gcomponent pixel is set to be the central pixel among the pixels in theBayer layout in the image sensor of the one-chip camera, the Rprediction tap (or the R class tap) or the B prediction tap (or the Bclass tap) is illustrated.

In FIG. 23C, in the case where the G prediction tap (or the G class tap)at the time of the generation of the G output image is such that thedifferent G component pixel is set to be the central pixel among thepixels in the Bayer layout in the image sensor of the one-chip camera,the R prediction tap (or the R class tap) or the B prediction tap (orthe B class tap) is illustrated.

In FIG. 23D, in the case where the G prediction tap (or the G class tap)at the time of the generation of the G output image is such that the Bcomponent pixel is set to be the central pixel among the pixels in theBayer layout in the image sensor of the one-chip camera, the Rprediction tap (or the R class tap) or the B prediction tap (or the Bclass tap) is illustrated.

FIGS. 24A and 24B are diagrams, each illustrating an example of the Rclass tap or the R prediction tap, or an example of the B class tap orthe B prediction tap in a case of converting the resolution of the imageconfigured from the pixels in the oblique Bayer layout, in the imageprocessing apparatus 150 that is described above referring to FIG. 12.In the examples in FIGS. 24A and 24B, the G class tap or the Gprediction tap is configured from nine (=3×3) pixels in which thecentral pixel is set to be the center. As described above, the imageprocessing apparatus 150 is first set to generate the G output image andthen is set to generate the R output image and the B output image usingthe generated G output image.

Moreover, in the image processing apparatus 150 that is described abovereferring to FIG. 12, the relationship between the class tap or theprediction tap in the case of converting the resolution of the imageconfigured from the pixels in the oblique Bayer layout and the positionof the pixel of the G output image is the same as in FIGS. 22A to 22B.

Therefore, in the image processing apparatus 150 that is described abovereferring to FIG. 12, the G prediction tap (or the G class tap) and theR prediction tap (or the R class tap) are different in structure fromeach other in the case of converting the resolution of the imageconfigured from the pixels in the oblique Bayer layout. Furthermore, theG prediction tap (or the G class tap) and the B prediction tap (or the Bclass tap) are different in structure from each other.

In FIGS. 24A and 24B, the pixel of the input image is indicated by acircle within which a letter R, G, or B is written, and four whitecircles in the vicinity of the circle within which the letter R, G, or Bis written indicate the pixels of the G output image. Then, in the Goutput image, what are indicated by dotted line circles are set to bethe class tap or the prediction tap, and a black dotted line circleindicates the central pixel of the class tap or the prediction tap.

In FIG. 24A, in the case where the G prediction tap (or the G class tap)at the time of the generation of the G output image is as illustrated inFIG. 22A, the R prediction tap (or the R class tap) or the B predictiontap (or the B class tap) is illustrated.

In FIG. 24B, in the case where the G prediction tap (or the G class tap)at the time of the generation of the G output image is as illustrated inFIG. 22B, the R prediction tap (or the R class tap) or the B predictiontap (or the B class tap) is illustrated.

Furthermore, also in the case of converting the resolution of the image,for example, the color difference is substituted for the pixel value,and thus the class classification, and the product and sum calculationmay be performed.

FIGS. 25A to 25D are diagrams for describing a relationship between theG class tap or the G prediction tap in the case of converting theresolution of the image configured from the pixels in the Bayer layoutand the position of the pixel of the G output image, in the imageprocessing apparatus 180 that is described above referring to FIG. 16.In FIGS. 25A to 25D, the class tap or the prediction tap is configuredfrom nine (=3×3) pixels in which the central pixel is set to be thecenter. Furthermore, at this point, in the case of the pixels in theBayer layout that is configured from a unit of four pixels (one Rcomponent pixel, one B component pixel, and two G component pixels), anexample of the structure of the class tap or the prediction tap in whicheach of the four unit pixels is set to be the central pixel isillustrated.

In FIGS. 25A to 25D, the pixel of the input image is indicated by acircle within which a letter R, G, or B is written, and four whitecircles in the vicinity of the circle within which the letter R, G, or Bis written indicate the pixels of the G output image.

As described above, the image processing apparatus 180 is first set togenerate the G output image and then is set to generate the R outputimage and the B output image using the generated G output image.However, the R class tap and the R prediction tap, and the B class tapand the B prediction tap are acquired directly from the input image.Furthermore, when the R output image and the B output image aregenerated using the generated G output image, the color difference issubstituted for the pixel value, and thus the class classification andthe product and sum calculation are set to be performed.

In FIG. 25A, among the pixels in the Bayer layout in the image sensor ofthe one-chip camera, the relationship is illustrated between the G classtap or the G prediction tap in the case where the R component pixel isset to be the central pixel and the position of the pixel of the Goutput image. In FIG. 25A, the pixel that is indicated by R writtenwithin a hatched circle is set to be the central pixel, and a smallblack circle is set to be the position of the pixel of the G outputimage that is generated from now on. Furthermore, in FIG. 25A, thepositions of all the pixels of the G output image are indicated by smallwhite circles for the sake of reference.

As illustrated in FIG. 25A, the pixels of the G output image arearranged in the four positions in the vicinity of the R component pixelthat is the central pixel. That is, four types of pixel values arepredicted (generated) based on the nine prediction taps illustrated inFIG. 25A.

In FIG. 25B, among the pixels in the Bayer layout in the image sensor ofthe one-chip camera, the relationship is illustrated between the G classtap or the G prediction tap in the case where the G component pixel isset to be the central pixel and the position of the pixel of the Goutput image. In FIG. 25B, the pixel that is indicated by G writtenwithin a hatched circle is set to be the central pixel, and a smallblack circle is set to be the position of the pixel of the G outputimage that is generated from now on. Furthermore, in FIG. 25B, thepositions of all the pixels of the G output image are indicated by smallwhite circles for the sake of reference.

As illustrated in FIG. 25B, the pixels of the G output image arearranged in the four positions in the vicinity of the G component pixelthat is the central pixel. That is, the four types of pixel values arepredicted (generated) based on the nine prediction taps illustrated inFIG. 25B.

Likewise, in FIG. 25C, among the pixels in the Bayer layout in the imagesensor of the one-chip camera, the relationship is illustrated betweenthe G class tap or the G prediction tap in the case where the differentG component pixel is set to be the central pixel and the position of thepixel of the G output image. In FIG. 25D, among the pixels in the Bayerlayout in the image sensor of the one-chip camera, the relationship isillustrated between the G class tap or the G prediction tap in the casewhere the B component pixel is set to be the central pixel and theposition of the pixel of the G output image.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures.

FIGS. 26A to 26D are diagrams for describing a relationship between theR class tap or the R prediction tap in the case of converting theresolution of the image configured from the pixels in the Bayer layoutand the position of the pixel of the R output image, in the imageprocessing apparatus 180 that is described above referring to FIG. 16.In each of the examples in FIGS. 26A to 26D, the class tap or theprediction tap is configured from the five pixels in the form of across.

Furthermore, at this point, in the case of the pixels in the Bayerlayout that is configured from a unit of four pixels (one R componentpixel, one B component pixel, and two G component pixels), an example ofthe structure of the class tap or the prediction tap in which each ofthe four unit pixels is set to be the central pixel is illustrated.

FIG. 26A is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Rcomponent pixel is set to be the central pixel.

In FIG. 26A, the pixel that is indicated by R written within a hatchedcircle is set to be the central pixel, and a small black circle is setto be the position of the pixel of the R output image that is generatedfrom now on. Furthermore, in FIG. 26A, the positions of all the pixelsof the R output image are indicated by small white circles for the sakeof reference.

As illustrated in FIG. 26A, the pixels of the R output image arearranged in the four positions in the vicinity of the R component pixelthat is the central pixel. That is, four types of pixel values arepredicted (generated) based on the five prediction taps illustrated inFIG. 26A.

FIG. 26B is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Gcomponent pixel is set to be the central pixel.

In FIG. 26B, the pixel that is indicated by G written within a hatchedcircle is set to be the central pixel, and a small black circle is setto be the position of the pixel of the R output image that is generatedfrom now on. Furthermore, in FIG. 26B, the positions of all the pixelsof the R output image are indicated by small white circles for the sakeof reference.

As illustrated in FIG. 26B, the pixels of the R output image arearranged in the four positions in the vicinity of the G component pixelthat is the central pixel. That is, the four types of pixel values arepredicted (generated) based on the five prediction taps illustrated inFIG. 26B.

In the same manner, FIG. 26C is a diagram illustrating an example of theclass tap or the prediction tap in the case where in the pixels in theBayer layout, the different G component pixel is set to be the centralpixel. FIG. 26D is a diagram illustrating an example of the class tap orthe prediction tap in the case where in the pixels in the Bayer layout,the B component pixel is set to be the central pixel.

As illustrated in FIGS. 26A to 26D, the five pixels that make up the Rclass tap or the R prediction tap are all set to be the R componentpixels, and do not include the G component pixel and the B componentpixel. Furthermore, in FIGS. 26B to 26D, the pixel that is the center ofthe R class tap or the R prediction tap is set to be the pixel in aposition adjacent to the original center pixel that is indicated by ahatched circle in the drawings.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures.

FIGS. 27A to 27D are diagrams for describing a relationship between theB class tap or the B prediction tap in the case of converting theresolution of the image configured from the pixels in the Bayer layoutand the position of the pixel of the B output image, in the imageprocessing apparatus 180 that is described above referring to FIG. 16.In each of the examples in FIGS. 27A to 27D, the class tap or theprediction tap is configured from the five pixels in the form of across.

Furthermore, at this point, in the case of the pixels in the Bayerlayout that is configured from a unit of four pixels (one R componentpixel, one B component pixel, and two G component pixels), an example ofthe structure of the class tap or the prediction tap in which each ofthe four unit pixels is set to be the central pixel is illustrated.

FIG. 27A is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Rcomponent pixel is set to be the central pixel.

In FIG. 27A, the pixel that is indicated by R written within a hatchedcircle is set to be the central pixel, and a small black circle is setto be the position of the pixel of the B output image that is generatedfrom now on. Furthermore, in FIG. 27A, the positions of all the pixelsof the B output image are indicated by small white circles for the sakeof reference.

As illustrated in FIG. 27A, the pixels of the B output image arearranged in the four positions in the vicinity of the R component pixelthat is the central pixel. That is, four types of pixel values arepredicted (generated) based on the five prediction taps illustrated inFIG. 27A.

FIG. 27B is a diagram illustrating an example of the class tap or theprediction tap in a case where in the pixels in the Bayer layer, the Gcomponent pixel is set to be the central pixel.

In FIG. 27B, the pixel that is indicated by G written within a hatchedcircle is set to be the central pixel, and a small black circle is setto be the position of the pixel of the B output image that is generatedfrom now on. Furthermore, in FIG. 27B, the positions of all the pixelsof the B output image are indicated by small white circles for the sakeof reference.

As illustrated in FIG. 27B, the pixels of the B output image arearranged in the four positions in the vicinity of the B component pixelthat is the central pixel. That is, the four types of pixel values arepredicted (generated) based on the five prediction taps illustrated inFIG. 27B.

In the same manner, FIG. 27C is a diagram illustrating an example of theclass tap or the prediction tap in the case where in the pixels in theBayer layout, the different G component pixel is set to be the centralpixel. FIG. 27D is a diagram illustrating an example of the class tap orthe prediction tap in the case where in the pixels in the Bayer layout,the B component pixel is set to be the central pixel.

As illustrated in FIGS. 27A to 27D, the five pixels that make up the Bclass tap or the B prediction tap are all set to be the B componentpixels, and do not include the G component pixel and the R componentpixel. Furthermore, in FIGS. 27A to 27C, the pixel that is the center ofthe B class tap or the B prediction tap is set to be the pixel in aposition adjacent to the original center pixel that is indicated by ahatched circle in the drawings.

Moreover, the class tap and the prediction tap may have the samestructures or may have different structures.

FIGS. 28A and 28B are diagrams illustrating an example of the R classtap or the R prediction tap in the case of converting the resolution ofthe image configured from the pixels in the oblique Bayer layout, in theimage processing apparatus 180 that is described above referring to FIG.16. As described above, the image processing apparatus 150 is first setto generate the G output image and then is set to generate the R outputimage and the B output image using the generated G output image.However, the R class tap and the R prediction tap, and the B class tapand the B prediction tap are acquired directly from the input image.Furthermore, when the R output image and the B output image aregenerated using the generated G output image, the color difference issubstituted for the pixel value, and thus the class classification andthe product and sum calculation are set to be performed.

Moreover, in the image processing apparatus 150 that is described abovereferring to FIG. 16, the relationship between the G class tap or the Gprediction tap in the case of converting the resolution of the imageconfigured from the pixels in the oblique Bayer layout and the positionof the pixel of the G output image is the same as in FIGS. 22A to 22B.

In FIGS. 28A and 28B, the pixel of the input image is indicated by acircle within which a letter R, G, or B is written, and four whitecircles in the vicinity of the circle within which the letter R, G, or Bis written indicate the pixels of the G output image, the pixels of theR output image or the pixels of the B output image.

In FIGS. 28A and 28B, the pixel that is indicated by G written within ahatched circle is set to be the central pixel, and a small black circleis set to be the position of the pixel of the R output image that isgenerated from now on. Furthermore, in FIGS. 28A and 28B, the positionsof all the pixels of the R output image are indicated by small whitecircles for the sake of reference. At this point, the R class tap or theR prediction tap is configured from the six pixels in the form of aright- or left-oblique rectangle.

In FIG. 28A, in the case where the G prediction tap (or the G class tap)is as illustrated in FIG. 22A, the R prediction tap or the R class tapis illustrated.

In FIG. 28B, in the case where the G prediction tap (or the G class tap)is as illustrated in FIG. 22B, the R prediction tap or the R class tapis illustrated.

As illustrated in FIGS. 28A and 28B, the pixels of the R output imageare arranged in the four positions in the vicinity of the G componentpixel that is the central pixel. That is, four types of pixel values arepredicted (generated) based on the six prediction taps illustrated inFIGS. 28A and 28B.

FIGS. 29A and 29B are diagrams illustrating an example of the B classtap or the B prediction tap in the case of converting the resolution ofthe image configured from the pixels in the oblique Bayer layout, in theimage processing apparatus 180 that is described above referring to FIG.16.

In FIGS. 29A and 29B, the pixel of the input image is indicated by acircle within which a letter R, G, or B is written, and four whitecircles in the vicinity of the circle within which the letter R, G, or Bis written indicate the pixels of the G output image, the pixels of theR output image or the pixels of the B output image.

In FIGS. 29A and 29B, the pixel that is indicated by G written within ahatched circle is set to be the central pixel, and a small black circleis set to be the position of the pixel of the R output image that isgenerated from now on. Furthermore, in FIGS. 29A and 29B, the positionsof all the pixels of the B output image are indicated by small whitecircles for the sake of reference. At this point, the B class tap or theB prediction tap is configured from the six pixels in the form of arectangle that is oblique to the left or to the right.

In FIG. 29A, in the case where the G prediction tap (or the G class tap)is as illustrated in FIG. 22A, the B prediction tap or the B class tapis illustrated.

In FIG. 29B, in the case where the G prediction tap (or the G class tap)is as illustrated in FIG. 22B, the B prediction tap or the B class tapis illustrated.

As illustrated in FIG. 29A or 29B, the pixels of the R output image arearranged in the four positions in the vicinity of the G component pixelthat is the central pixel. That is, four types of pixel values arepredicted (generated) based on the six prediction taps illustrated inFIG. 29A or 29B.

In this manner, according to the present technology, each of the pixelvalues of the G output images with the different resolutions, the Routput image, and the B output image also can be generated (predicted)based on the pixel value of the image corresponding to the image signalthat is output from the image sensor of the one-chip camera. Forexample, at this time, for example, the resolution of the imagecorresponding to the image signal that is output from the image sensorin the oblique Bayer arrangement, in which the pixel density is set tobe increased, can be converted by obliquely changing the arrangement ofthe pixels in the Bayer layout in the image sensor.

Moreover, a sequence of processing operations described above may beexecuted in hardware and may be executed in software. If the sequence ofprocessing operations described above is executed in software, a programfor executing the sequence of processing operations in software isinstalled from a recording medium or over a network on a computer thatis built into dedicated hardware, or on a general-purpose personalcomputer 700 capable of executing various functions by installingvarious programs, for example one as illustrated in FIG. 30.

In FIG. 30, a central processing unit (CPU) 701 executes variousprocessing operations according to the program that is stored in a readonly memory (ROM) 702 or the program that is loaded from a storage unit708 onto a random access memory (RAM) 703. Data, necessary for CPU 701to perform various processing operations, and the like are properlystored in RAM 703.

CPU 701, ROM 702, and RAM 703 are connected to one another through a bus704. An input and output interface 705 is also connected to the bus 704.

To the input and output interface 705 is connected an input unit 706,such as a keyboard or a mouse, a display, made from a liquid crystaldisplay (LCD) and the like, an output unit 707, made from a speaker andthe like, a storage unit 708, configured from a hard disk and the like,and a communication unit 709, configured from a network interface card,such as a modem, or a LAN card. The communication unit 709 performscommunication processing over networks including the Internet.

A drive 710 is connected to the input and output interface 705 whenevernecessary, and a magnetic disk, an optical disc, an optical magneticdisc, or a removable medium 711 such as a semiconductor memory isproperly mounted to the input and output interface 705. A computerprogram that is read from these is installed on the storage unit 708whenever necessary.

If the sequence of processing operations described above is executed insoftware, the program for executing the sequence of processingoperations in software is installed over a network such as the Internet,or through the recording medium, made from the removable medium 711.

Moreover, the recording medium is configured from a magnetic disk(including a floppy disk (registered trademark)), an optical disc(including a Compact Disc-Read Only Memory (CD-ROM), and a digitalversatile disc (DVD)), an optical magnetic disc (including a Mini-Disc(MD) (registered trademark)), or the removable medium 711, made from asemiconductor memory and the like in which the program is stored, whichis distributed in order to deliver the program to a user separately froma main body of an apparatus illustrated in FIG. 30. In addition, therecording medium is configured from ROM 702 or the hard disk included inthe storage unit 708, which is delivered to a user in a state where itis in advance built into the main body of the apparatus and on which theprogram is stored.

In the present disclosure, the sequence of processing operationdescribed above includes not only processing that is performed in timeseries according to an order created, but also processing that althoughnecessarily performed in time series, is performed in parallel orindividually.

Moreover, embodiments of the present technology are not limited to theembodiments described above and various modifications can be made withina scope not departing from the gist of the present technology.

Moreover, the present technology can have the following configurations.

(1)

An image processing apparatus including a representative valuecalculation unit that selects a designation area that is an area whichis configured from a predetermined number of pixels, from a first imagewhich is configured by using an image signal which is output from aone-chip pixel unit in which pixels corresponding to each colorcomponent in multiple color components are regularly arranged on aplane, and that calculates a representative value of each of the colorcomponents in the designation area; a class classification unit thatperforms class classification on the designation area, based on anamount of characteristics that are obtained from a pixel value of thedesignation area; a coefficient reading unit that reads a coefficientthat is stored in advance, based on a result of performing the classclassification; a color component conversion unit that sets the pixelvalue relating to a predetermined pixel within the designation area tobe a prediction tap, sets the pixel value of one color component, amongthe multiple color components, to be a reference, and converts the pixelvalue of each color component of the prediction tap into a conversionvalue that is obtained by performing offset using the representativevalue; and a product and sum calculation unit that sets the conversionvalue to be a variable and calculates each of the pixel values of asecond image which is configured from only the pixels corresponding toeach color component in the multiple color components and which is animage different in resolution from the first image, by performingproduct and sum calculation which uses the coefficient which is read.

(2)

The image processing apparatus according to (1), in which the one-chippixel unit is a pixel unit that has R, G, and B color components; and inwhich the representative value calculation unit calculates aninterpolation value g of the R or B pixel, based on the G pixel in thevicinity of the R or B pixel, calculates an interpolation value r and aninterpolation value b of each of the G pixels, based on the R pixel orthe B pixel in the vicinity of the G pixel, calculates therepresentative value of G by using an average value of an input value Gobtained directly from the G pixel and the interpolation value g,calculates the representative value of R, based on a difference betweenthe interpolation value r and the input value G and a difference betweenthe input value R directly obtained from the R pixel and theinterpolation value g, and the representative value of the G, andcalculates the representative value of B, based on a difference betweenthe interpolation value b and the input value G and a difference betweenthe input value B obtained directly from the B pixel and interpolationvalue g, and the representative value of the G.

(3)

The image processing apparatus according to (2), in which if the secondimage is an image that is configured from only the G pixels, the colorcomponent conversion unit offsets the input value R by using adifference between the representative value of the R and therepresentative value of the G, and offsets the input value B by using adifference between the representative value of the B and therepresentative value of the G; in which if the second image is an imagethat is configured from only the R pixels, the color componentconversion unit offsets the input value G by using a difference betweenthe representative value of the G and the representative value of the R,and offsets the input value B by using a difference between therepresentative value of the B and the representative value of the R; andin which if the second image is an image that is configured from onlythe B pixels, the color component conversion unit offsets the inputvalue G by using a difference between the representative value of the Gand the representative value of the B, and offsets the input value R byusing a difference between the representative value of the R and therepresentative value of the B.

(4)

The image processing apparatus according to (3), in which the one-chipimage unit is set to be a pixel unit in an oblique Bayer layout in whichthe pixels in the Bayer layout are obliquely arranged.

(5)

The image processing apparatus according to any one of (1) to (4), inwhich if the second image that is configured from only first colorcomponents is generated, among the images with the multiple colorcomponents, and the second image that is configured from only secondcolor components different from the first color components is generated,among the images with the multiple color components, the prediction tapis acquired from the second image that is configured from only the firstcolor components.

(6)

The image processing apparatus according to any one of (1) to (5),further including a virtual color difference calculation unit thatcalculates a virtual color difference of the prediction tap, in which ifthe second image that is configured from only the second colorcomponents different from the first color components is generated amongthe images with the multiple color components, the product and sumcalculation unit sets the virtual color difference of the prediction tapto be the variable and calculates the virtual color difference of thesecond image by performing the product and sum calculation that uses thecoefficient that is read; and in which the prediction tap that isconfigured from only the pixels corresponding to the second colorcomponent is acquired from the designation area in the first image.

(7)

The image processing apparatus according to (6), in which the virtualcolor difference calculation unit calculates the virtual colordifference by multiplying the value of the pixel that makes up theprediction tap by a matrix coefficient that is stipulated byspecification for color space.

(8)

The image processing apparatus according to any one of (1) to (7),further including a different color component conversion unit that setsthe pixel value relating to a predetermined pixel within the designationarea to be a class tap, sets the pixel value of one color component,among the multiple color components, to be a reference, and converts thepixel value of each color component of the class tap into a conversionvalue that is obtained by performing offset using the representativevalue, in which the class classification unit determines an amount ofcharacteristics of the class tap, based on the conversion value thatresults from the conversion by the different color component conversionunit.

(9)

The image processing apparatus according to any one of (1) to (8), inwhich the coefficient that is read by the coefficient reading unit isobtained by prior learning; in which in the prior learning, the imagethat is configured by using each of the image signals that are outputfrom the multiple pixel units, which are arranged in a position near aphotographic subject, and each of which is configured from only thepixels corresponding to each of the multiple color components is set tobe a teacher image by using an optical low pass filter that is arrangedbetween the one-chip pixel unit and the photographic subject; in whichthe image that is configured by using the image signal that is outputfrom the one-chip pixel unit is set to be a student image; and in whichthe coefficient is calculated by solving a normal equation in which thepixel of the student image and the pixel of the teacher image are mappedto each other.

(10)

An image processing method including enabling a representative valuecalculation unit to select a designation area that is an area which isconfigured from a predetermined number of pixels, from a first imagewhich is configured by using an image signal which is output from aone-chip pixel unit in which pixels corresponding to each colorcomponent in multiple color components are regularly arranged on aplane, and to calculate a representative value of each of the colorcomponents in the designation area; enabling a class classification unitto perform class classification on the designation area, based on anamount of characteristics that are obtained from a pixel value of thedesignation area; enabling a coefficient reading unit to read acoefficient that is stored in advance, based on a result of performingthe class classification; enabling a color component conversion unit toset the pixel value relating to a predetermined pixel within thedesignation area to be a prediction tap, to set the pixel value of onecolor component, among the multiple color components, to be a reference,and to convert the pixel value of each color component of the predictiontap into a conversion value that is obtained by performing offset usingthe representative value; and enabling a product and sum calculationunit to set the conversion value to be a variable and to calculate eachof the pixel values of a second image which is configured from only thepixels corresponding to each color component in the multiple colorcomponents and which is an image different in resolution from the firstimage, by performing product and sum calculation which uses thecoefficient which is read.

(11)

A program for causing a computer to function as an image processingapparatus including a representative value calculation unit that selectsa designation area that is an area which is configured from apredetermined number of pixels, from a first image which is configuredby using an image signal which is output from a one-chip pixel unit inwhich pixels corresponding to each color component in multiple colorcomponents are regularly arranged on a plane, and that calculates arepresentative value of each of the color components in the designationarea; a class classification unit that performs class classification onthe designation area based on an amount of characteristics that areobtained from a pixel value of the designation area; a coefficientreading unit that reads a coefficient that is stored in advance based ona result of performing the class classification; a color componentconversion unit that sets the pixel value relating to a predeterminedpixel within the designation area to be a prediction tap, sets the pixelvalue of one color component, among the multiple color components, to bea reference, and converts the pixel value of each color component of theprediction tap into a conversion value that is obtained by performingoffset using the representative value; and a product and sum calculationunit that sets the conversion value to be a variable and calculates eachof the pixel values of a second image which is configured from only thepixels corresponding to each color component in the multiple colorcomponents and which is an image which is different in resolution fromthe first image, by performing product and sum calculation which usesthe coefficient which is read.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

What is claimed is:
 1. An image processing apparatus comprising: arepresentative value calculation unit that selects a designation areathat is an area which is configured from a predetermined number ofpixels, from a first image which is configured by using an image signalwhich is output from a one-chip pixel unit in which pixels correspondingto each color component in multiple color components are regularlyarranged on a plane, and that calculates a representative value of eachof the color components in the designation area; a class classificationunit that performs class classification on the designation area, basedon an amount of characteristics that are obtained from a pixel value ofthe designation area; a coefficient reading unit that reads acoefficient that is stored in advance, based on a result of performingthe class classification; a color component conversion unit that setsthe pixel value relating to a predetermined pixel within the designationarea to be a prediction tap, sets the pixel value of one colorcomponent, among the multiple color components, to be a reference, andconverts the pixel value of each color component of the prediction tapinto a conversion value that is obtained by performing offset using therepresentative value; and a product and sum calculation unit that setsthe conversion value to be a variable and calculates each of the pixelvalues of a second image which is configured from only the pixelscorresponding to each color component in the multiple color componentsand which is an image different in resolution from the first image, byperforming product and sum calculation which uses the coefficient whichis read.
 2. The image processing apparatus according to claim 1, whereinthe one-chip pixel unit is a pixel unit that has R, G, and B colorcomponents, and wherein the representative value calculation unit,calculates an interpolation value g of the R or B pixel, based on the Gpixel in the vicinity of the R or B pixel, calculates an interpolationvalue r and an interpolation value b of each of the G pixels, based onthe R pixel or the B pixel in the vicinity of the G pixel, calculatesthe representative value of G by using an average value of an inputvalue G obtained directly from the G pixel and the interpolation valueg, calculates the representative value of R, based on a differencebetween the interpolation value r and the input value G and a differencebetween the input value R directly obtained from the R pixel and theinterpolation value g, and the representative value of the G, andcalculates the representative value of B, based on a difference betweenthe interpolation value b and the input value G and a difference betweenthe input value B obtained directly from the B pixel and interpolationvalue g, and the representative value of the G.
 3. The image processingapparatus according to claim 2, wherein if the second image is an imagethat is configured from only the G pixels, the color componentconversion unit offsets the input value R by using a difference betweenthe representative value of the R and the representative value of the G,and offsets the input value B by using a difference between therepresentative value of the B and the representative value of the G,wherein if the second image is an image that is configured from only theR pixels, the color component conversion unit offsets the input value Gby using a difference between the representative value of the G and therepresentative value of the R, and offsets the input value B by using adifference between the representative value of the B and therepresentative value of the R, and wherein if the second image is animage that is configured from only the B pixels, the color componentconversion unit offsets the input value G by using a difference betweenthe representative value of the G and the representative value of the B,and offsets the input value R by using a difference between therepresentative value of the R and the representative value of the B. 4.The image processing apparatus according to claim 3, wherein theone-chip image unit is set to be a pixel unit in an oblique Bayer layoutin which the pixels in the Bayer layout are obliquely arranged.
 5. Theimage processing apparatus according to claim 1, wherein if the secondimage that is configured from only first color components is generated,among the images with the multiple color components, and the secondimage that is configured from only second color components differentfrom the first color components is generated, among the images with themultiple color components, the prediction tap is acquired from thesecond image that is configured from only the first color components. 6.The image processing apparatus according to claim 1, further comprising:a virtual color difference calculation unit that calculates a virtualcolor difference of the prediction tap, wherein if the second image thatis configured from only the second color components different from thefirst color components is generated among the images with the multiplecolor components, the product and sum calculation unit sets the virtualcolor difference of the prediction tap to be the variable and calculatesthe virtual color difference of the second image by performing theproduct and sum calculation that uses the coefficient that is read, andwherein the prediction tap that is configured from only the pixelscorresponding to the second color component is acquired from thedesignation area in the first image.
 7. The image processing apparatusaccording to claim 6, wherein the virtual color difference calculationunit calculates the virtual color difference by multiplying the value ofthe pixel that makes up the prediction tap by a matrix coefficient thatis stipulated by specification for color space.
 8. The image processingapparatus according to claim 1, further comprising: a different colorcomponent conversion unit that sets the pixel value relating to apredetermined pixel within the designation area to be a class tap, setsthe pixel value of one color component, among the multiple colorcomponents, to be a reference, and converts the pixel value of eachcolor component of the class tap into a conversion value that isobtained by performing offset using the representative value, whereinthe class classification unit determines an amount of characteristics ofthe class tap, based on the conversion value that results from theconversion by the different color component conversion unit.
 9. Theimage processing apparatus according to claim 1, wherein the coefficientthat is read by the coefficient reading unit is obtained by priorlearning, wherein in the prior learning, the image that is configured byusing each of the image signals that are output from the multiple pixelunits, which are arranged in a position near a photographic subject, andeach of which is configured from only the pixels corresponding to eachof the multiple color components is set to be a teacher image by usingan optical low pass filter that is arranged between the one-chip pixelunit and the photographic subject, wherein the image that is configuredby using the image signal that is output from the one-chip pixel unit isset to be a student image, and wherein the coefficient is calculated bysolving a normal equation in which the pixel of the student image andthe pixel of the teacher image are mapped to each other.
 10. An imageprocessing method comprising: enabling a representative valuecalculation unit to select a designation area that is an area which isconfigured from a predetermined number of pixels, from a first imagewhich is configured by using an image signal which is output from aone-chip pixel unit in which pixels corresponding to each colorcomponent in multiple color components are regularly arranged on aplane, and to calculate a representative value of each of the colorcomponents in the designation area; enabling a class classification unitto perform class classification on the designation area, based on anamount of characteristics that are obtained from a pixel value of thedesignation area; enabling a coefficient reading unit to read acoefficient that is stored in advance, based on a result of performingthe class classification; enabling a color component conversion unit toset the pixel value relating to a predetermined pixel within thedesignation area to be a prediction tap, to set the pixel value of onecolor component, among the multiple color components, to be a reference,and to convert the pixel value of each color component of the predictiontap into a conversion value that is obtained by performing offset usingthe representative value; and enabling a product and sum calculationunit to set the conversion value to be a variable and to calculate eachof the pixel values of a second image which is configured from only thepixels corresponding to each color component in the multiple colorcomponents and which is an image different in resolution from the firstimage, by performing product and sum calculation which uses thecoefficient which is read.
 11. A program for causing a computer tofunction as an image processing apparatus comprising: a representativevalue calculation unit that selects a designation area that is an areawhich is configured from a predetermined number of pixels, from a firstimage which is configured by using an image signal which is output froma one-single pixel unit in which pixels corresponding to each colorcomponent in multiple color components are regularly arranged on aplane, and that calculates a representative value of each of the colorcomponents in the designation area, a class classification unit thatperforms class classification on the designation area, based on anamount of characteristics that are obtained from a pixel value of thedesignation area, a coefficient reading unit that reads a coefficientthat is stored in advance, based on a result of performing the classclassification, a color component conversion unit that sets the pixelvalue relating to a predetermined pixel within the designation area tobe a prediction tap, sets the pixel value of one color component, amongthe multiple color components, to be a reference, and converts the pixelvalue of each color component of the prediction tap into a conversionvalue that is obtained by performing offset using the representativevalue, and a product and sum calculation unit that sets the conversionvalue to be a variable and calculates each of the pixel values of asecond image which is configured from only the pixels corresponding toeach color component in the multiple color components and which is animage different in resolution from the first image, by performingproduct and sum calculation which uses the coefficient which is read.